In Nigeria, geospatial is increasingly a capability inside other careers, such as surveying, planning, engineering, agriculture and software, rather than a standalone one, though dedicated roles still exist. The most durable position is a strong parent discipline with geospatial as your edge.

How it got here
When I started, GIS barely existed as a standalone job. You were a surveyor who used GIS, a geologist who used GIS, a town planner who used GIS. Through the 2000s, organisations began hiring dedicated GIS officers, in ministries, oil companies, environmental consultancies and banks, and for a while it looked like a profession with its own career ladder. Many of us built our careers on that bet.

Why the pendulum is swinging back

The tools got easier. When GIS meant weeks of training and expensive software, specialisation paid. Now an engineer can pick up QGIS in a weekend, an analyst with Python can build a pipeline without calling themselves a “GIS person”, and a field officer collects data with a phone app. When the tool is widespread, being the tool-holder is no longer scarce.
Employers buy outcomes, not technologies. A ministry wants flood risk mapped, properties identified and infrastructure planned. If a capable officer with a laptop delivers that, the job title matters less. In my experience, GIS units are often folded into ICT or planning departments.
The strongest options go to hybrids. The people with the most choices are often professionals with a strong parent discipline who can also build a spatial database, run satellite analysis or ship a mapping product. For them, geospatial is a multiplier, not an identity.

What the job data shows
Python appeared in 27% of the [N] geospatial job postings GeoHubNG analysed between [start date] and [end date]. Most postings did not ask for it, but the share is large enough that professionals who code have more options. [One line on where the postings were collected from.]

What I’d tell a young person in 2026

Anchor in a discipline first. Survey, planning, agriculture, engineering, environmental science, computer science: pick one and get genuinely good at it. Geospatial on top of a weak base makes you the person who produces maps nobody asked for.
Let geospatial be your edge, not your cage. The market rewards hybrids.
Own a domain, such as land administration, agriculture, logistics, insurance or mining. Deep sector knowledge is harder to commoditise than tool skills.
Build things. A portfolio of real projects, like a flood map that mattered or a data product people use, often counts for more than a certificate.

Where this leaves a platform like GeoHubNG
If geospatial is a capability spread across surveying firms, ministries, agritech startups and banks, those professionals need a common home. A community built around one tool is fragile. One built around an industry’s shared needs, such as jobs, data, companies and knowledge, is more durable.

So is it a career? Both. Dedicated roles still exist, but they are narrower, and the more resilient path is to treat geospatial as a deep capability anchored in a domain. That doesn’t guarantee work, but it is the strongest position I know.

By A. B. Wayforward

Land-cover and building-detection models trained mostly on data from other regions often perform worse in Nigeria, because the landscape, the seasons, the haze and the labels differ from what the models learned. Local labels, seasonal time series and field validation are the practical fixes.

Why it happens

Training data
A review of Earth-observation datasets found that nearly 40% cover only Europe or North America, while Africa accounts for about 5%. Benchmarks of geospatial foundation models report weaker results in under-represented regions such as Africa, and models trained on one continent can perform very differently on another. A model that scores well on a benchmark has not necessarily seen anything like your study area.
The built environment
Nigerian compounds mix materials: mud-brick, zinc-roofed and unfinished concrete structures can share one compound. Google’s Open Buildings team notes that buildings the same colour as their surroundings and very small buildings are hard to detect at satellite resolution, and that large or complex buildings are sometimes split into several detections. [Optional, only if it comes from your own testing: In my own tests of an open building-detection model on Kano imagery, it missed roughly 40% of structures despite strong benchmark scores.]
Seasons, haze and cloud
Land-cover signals depend on climate and season. A smallholder mosaic of maize, sorghum, fallow and grazing can interleave at scales finer than a satellite pixel, which a model trained on large, uniform fields will misread. During the harmattan, dust haze degrades optical imagery over the north for weeks. In the south, persistent cloud causes a similar problem.
The labels
Definitions differ between products. What counts as “forest” or “cropland” in a global dataset may not match savanna woodland, farm fallow or plantation mosaics on the ground, so a model can follow its training faithfully and still be wrong locally.

What works, in order of effort

Check what already exists. Google’s Open Buildings is Africa-focused, trained on 1.75 million manually labelled buildings from imagery across the continent, and each building carries a confidence score you can filter on. GRID3 Nigeria Settlement Extents (v4.1, released August 2026, CC BY-SA 4.0) maps settlements and blocks countrywide, and its v4.0 update removed 2,269 settlements that fieldwork showed were false detections. Use these as baselines and check their licences, but treat none of them as truth.
Fine-tune on local labels. Needs vary by task, but the numbers can be modest: in a multi-continent crop-mapping study, about 100 labelled images gave high overall accuracy, and roughly 900 were needed to handle class imbalance.
Use time, not snapshots. A full year of NDVI typically separates fallow from cropped land better than any single image. Where haze or cloud blocks optical data, radar such as Sentinel-1 can fill the gaps.
Validate with local ground truth, and report accuracy by region and season instead of a single headline score. A community member who knows which plots lay fallow last season is worth more than a benchmark percentage.

The bigger point
Satellites pass over Nigeria constantly, but coverage is not capability. Capability comes from validated local labels, local expertise and models checked against Nigerian landscapes. Sentinel and Landsat imagery and tools like Python and QGIS are free, but the very-high-resolution imagery that building detection needs is not. In my view, validated Nigerian training datasets for crops, land cover and compound-level buildings are among the most valuable things a practitioner here could build.

By A. B Wayforward

About the Position

The Geospatial Analytics and Remote Sensing Intern will support ACS’s spatial analysis, climate mapping, remote sensing applications, and geospatial data processing activities. This internship offers an opportunity to apply advanced GIS and Earth observation techniques to real-world climate challenges.

Specific Terms of Reference include

Process satellite and GIS datasets.
Produce climate hazard and vulnerability maps.
Support spatial modelling and visualization.
Develop web-ready maps and dashboards.
Assist in land use and environmental change analyses.
Support quality assurance of spatial datasets.
Prepare technical documentation.
Requirements

Final-year MSc student or PhD candidate in GIS, Remote Sensing, Geography, Geoinformatics, Environmental Science, or related disciplines.
Experience with ArcGIS, QGIS, Google Earth Engine, R, or Python.
Excellent academic performance
Strong analytical and quantitative skills.
Terms of Employment

Home-based internship.
Duration: 3–6 months (renewable based on performance).
Flexible working hours compatible with academic commitments.
Mentorship by experienced professionals.
Certificate of completion and recommendation letter upon successful completion.
Outstanding interns may be considered for future employment opportunities.

Tasks:

Review project requirements, client documentation, source datasets, reference geographies, and existing geospatial platform capabilities to define the geospatial implementation baseline.
Assess geospatial data readiness, including raster datasets, vector layers, administrative boundaries, sub-national datasets, coordinate systems, spatial metadata, and known data quality constraints.
Design and implement geospatial data preparation workflows, including geometry validation, coordinate reference system handling, reprojection, format normalization, clipping, joining, aggregation, simplification, enrichment, and spatial quality checks.
Support raster and vector data processing requirements, including raster preparation, vector layer management, spatial indexing, tiling considerations, and preparation of datasets for downstream analytics, APIs, map services, or visualization layers.
Participate in technical discussions with client stakeholders to clarify geospatial requirements, confirm assumptions, assess feasibility, explain limitations, and support acceptance of delivered outputs.
Support the analysis, design, implementation, validation, and documentation of geospatial capabilities for the current project and future related initiatives.
Implement or configure agreed geoprocessing functions, which may include buffering, proximity analysis, spatial aggregation, pattern analysis, classification, composite indexing, clipping, spatial joins, and sub-national processing.
Support the preparation and publication of geospatial outputs through agreed mechanisms, such as GeoJSON, Shapefile, CSV with coordinates, GeoTIFF where applicable, map services, OGC aligned service patterns, APIs, or export packages.
Provide technical input to API and interoperability teams on geospatial service requirements, including OGC WMS, WFS, WCS, OGC API patterns, GeoJSON, metadata alignment, and geospatial API payload structures where applicable.
Work with data engineering and data architecture teams to ensure geospatial datasets are correctly integrated into ingestion, transformation, validation, metadata, governance, and publication workflows.
Support geospatial metadata, provenance, quality, and lineage requirements in coordination with data governance and catalogue teams.
Define and execute geospatial validation activities, including sample maps, spatial accuracy checks, comparison against reference layers, export validation, format validation, and documentation of known limitations.
Advise on geospatial performance considerations, including map loading, spatial query patterns, export size, raster processing complexity, tile generation, and service consumption patterns.
Prepare clear technical documentation, configuration notes, supported format descriptions, workflow diagrams, validation evidence, operational considerations, and handover materials for technical teams.

Requirements:

Education: Advanced university degree in Geographic Information Systems, Geography, Geomatics, Geospatial Science, Environmental Science, Computer Science, Data Science, Engineering, or a related field.
Professional Experience: Proven professional experience in GIS, geospatial data engineering, spatial analytics, or geospatial solution implementation.
Geospatial Data Experience: Hands-on experience with raster and vector datasets, administrative boundaries, sub-national data, coordinate reference systems, spatial metadata, and geospatial data quality controls.
Implementation Experience: Experience designing and implementing geospatial workflows such as data preparation, reprojection, geometry validation, spatial joins, aggregation, clipping, proximity analysis, buffering, raster processing, or map layer publication.
Standards and Interoperability: Experience with common GIS formats and standards, including GeoJSON, Shapefile, KML/KMZ, CSV with coordinates, GeoTIFF, and web mapping or geospatial service patterns such as WMS, WFS, WCS, OGC API Features, and OGC API Tiles.

Designs and operationalizes geospatial AI pipelines and models that transform Earth observation data into policy-relevant indicators and decision-support tools for Arab Member States.

Job Opening ID: 285275

A high-resolution WorldPop raster dataset estimating Nigeria’s population by sex and age group for 2025. The data are provided at approximately 100 m resolution in GeoTIFF format and include age groups from infants through older age categories. Publisher: WorldPop, University of Southampton.

Location: Ode-Remo, Ogun State
Industry: Space & Satellite Technology
Employment Type: Full-Time | On-site

Proforce Galaxies is a forward-looking advanced space technology and defense-focused company committed to delivering advanced satellite and space-based solutions that support national security, strategic intelligence, and critical infrastructure protection. Our capabilities are centered on the design, development, and deployment of high-performance systems that enable secure communications, real-time surveillance, and data-driven decision-making. At Proforce Galaxies, we are dedicated to strengthening technological sovereignty, enhancing situational awareness, and advancing aerospace innovation to support mission-critical operations and long-term national and global security objectives.

We are seeking a talented and passionate Computer Vision (AI) Engineer to join our growing team of experts. This role will be instrumental in building intelligent systems that transform satellite imagery into actionable insights, enabling data-driven decisions across multiple industries.

Position Overview

As a Computer Vision (AI) Engineer, you will be responsible for designing and implementing state-of-the-art computer vision algorithms tailored for satellite imagery analysis. Your work will directly impact applications such as object detection, environmental monitoring, change detection, and geospatial intelligence.

You will work collaboratively with GIS Analysts, Fullstack Developers, and other team members to deploy scalable AI solutions within real-world platforms. This is a high-impact role for someone who thrives on innovation, research, and hands-on development.

Key Responsibilities

Develop advanced computer vision models for analyzing satellite imagery (e.g., segmentation, object detection, pattern recognition, change detection).
Train, fine-tune, and optimize deep learning models (CNNs, Transformers, etc.) using frameworks such as TensorFlow and PyTorch.
Process multispectral, SAR, and optical satellite imagery to extract relevant features for downstream applications.
Collaborate with Software Developers to deploy models to production environments (cloud-based or edge devices).
Integrate AI-generated insights into geospatial workflows in partnership with GIS Analysts.
Optimize computer vision algorithms for performance, scalability, and low-latency edge processing.
Validate model accuracy, robustness, and reliability using real-world satellite datasets.
Support web and mobile applications by developing AI-driven features and capabilities.
Maintain comprehensive documentation of models, data pipelines, training experiments, and deployment processes.
Stay informed of the latest research, tools, and technologies in computer vision and satellite data analytics.
Qualifications & Skills

Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Artificial Intelligence, Remote Sensing, or related fields.
Proven experience in computer vision and deep learning applied to imagery data.
Strong programming skills in Python; proficiency in deep learning libraries like TensorFlow, Keras, or PyTorch.
Experience working with satellite data (e.g., multispectral, SAR, hyperspectral) is highly desirable.
Familiarity with geospatial data formats and tools (e.g., GDAL, QGIS, GeoTIFF).
Knowledge of cloud computing platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes) is a plus.
Ability to work independently and collaboratively in cross-functional teams.
Strong analytical, problem-solving, and communication skills.
What We Offer

A chance to be part of a groundbreaking journey in African space technology.
A dynamic and collaborative work environment.
Opportunities for professional growth and skill development.
Access to innovative projects with real-world impact.
Competitive salary and benefits package.
Only shortlisted candidates will be contacted

Pay: ₦250,000.00 – ₦350,000.00 per month

Work Location: In person

Suvoda is seeking a skilled and driven Senior Full-Stack Software Engineer to help build and evolve a reporting web application used to view, query, download, and schedule delivery of reports. You’ll own features end to end: a Python backend and a React/TypeScript frontend.

What Should You Expect?

Responsibilities:

Backend:

– Design and build backend services using Python and a modern web framework (e.g. FastAPI), exposing REST APIs consumed by the frontend and other client applications.

– Implement authentication and authorization: SSO integration, session/token handling, and role/permission-based access control.

– Implement audit logging for key user actions (who/when/what/why) and change tracking across core data.

– Build and maintain scheduling and background job features (e.g. cron-based schedules, retries, status tracking).

– Own database schema design and changes, using a migration tool to manage schema evolution.

– Write automated backend tests and uphold code quality and linting standards as part of a test-driven workflow.

Frontend:

– Design and build UI features in React and TypeScript, covering configuration screens, data views, and dashboards.

– Build interactive dashboards and data grids, including custom, user-configurable layouts.

– Implement frontend session handling and permission-aware UI (hide/disable actions the current user isn’t allowed to perform).

– Own the full request lifecycle for a feature — from API contract design, through backend implementation, to the frontend components and state management that consume it.

– Write frontend component/integration tests and maintain type safety.

AI Features:

– Build and maintain AI-powered features (e.g. an in-app AI assistant panel, AI-driven insights/recommendations) on both the API and UI layers.

– Integrate with LLM/AI services through approved, governed integrations — respecting data-handling policies (no confidential or customer data sent to non-approved services) and keeping AI output reviewable/attributable to a human rather than auto-applied.

– Use AI coding assistants (e.g. GitHub Copilot, Claude Code) as part of the day-to-day development workflow to speed up delivery while maintaining code quality and test coverage.

Platform & Delivery:

– Containerize and deploy services (e.g. Docker, Kubernetes/Helm) across multiple environments.

– Provision and manage cloud infrastructure as code using Terraform.

– Build and maintain CI/CD pipelines with GitHub Actions: automated tests, security scanning, image builds, and deployment automation.

– Collaborate with product, engineering, and QA to deliver reusable, secure solutions, and support code quality, observability, and API design best practices.

Requirements:

– Bachelor’s degree in a technical field such as Computer Science or a related discipline.

– At least 7 years of experience in full-stack software engineering, with demonstrated ownership of production systems spanning backend and frontend.

– Strong proficiency in Python, including experience building production REST APIs and working with an ORM.

– Strong proficiency in React and TypeScript, including hooks-based components, client-side data fetching/caching, and routing.

– Strong SQL skills and experience with relational data modeling.

– Understanding of authentication/authorization patterns (SSO, tokens/JWT) and role-based permission models, implemented consistently across API and UI layers.

– Experience with containerization and CI/CD pipelines (GitHub Actions or similar).

– Working knowledge of infrastructure-as-code (Terraform or similar).

– Comfortable using AI coding assistants (e.g. GitHub Copilot, Claude Code, Cursor) in daily development, and able to critically review AI-generated code rather than accept it blindly.

– Strong communication and collaboration skills, comfortable owning a feature end to end across backend and frontend.

#LI-AA1

We are aware that an individual(s) are fraudulently representing themselves as Suvoda recruiters and/or hiring managers. Suvoda will never request personal information such as your bank account number, credit card number, drivers license or social security number — or request payment from you — during the job application or interview process. Any emails from the Suvoda recruiting team will come from a @suvoda.com email address. You can learn more about these types of fraud by referring to this FTC consumer alert.

Who We Are:

Galaxy Digital Inc. (Nasdaq: GLXY) is a global leader in digital assets and data center infrastructure, growing the economy that runs on code. Galaxy delivers the onchain infrastructure that connects institutions to digital assets, including trading, advisory, asset management, staking, self-custody, and tokenization. Galaxy also develops and operates data center infrastructure to power AI and HPC workloads. Anchored by its Helios campus in Texas, Galaxy is building a multi-gigawatt pipeline of more than 5.7 GW of potential capacity, positioning it among the largest and fastest-growing data center developers in North America.

The Company is headquartered in New York City, with offices across North America, Europe, the Middle East, and Asia.

Additional information about Galaxy’s businesses and products is available on www.galaxy.com.

What We Value:

We are a diverse team of free thinkers, and fast movers united to help investors and creators energize the global economy. We are looking for individuals who thrive in a culture of builders and overachievers and embrace high performance, transparent feedback, and a mission-first approach. Our culture shapes our way of working and gets us where we want to be.

• Seek Excellence.
• Be Selective To Be Effective.
• Be Highly Aligned, Loosely Coupled.
• Disagree Transparently.
• Encourage Independent Decision-Making.
• Build Dream Teams.

About the Internship Program

The Galaxy Internship Program offers students the opportunity to gain hands-on, practical experience while contributing to meaningful work across the business. As a Cybersecurity Intern on Galaxy’s Product Security team, you will help support the development and refinement of secure engineering standards used by our software and production engineering teams. These standards may include areas such as authentication and authorization, secure API communication, web UI security, and secure coding practices. The intern will help enhance existing guidance with more specific implementation details and may support the development of custom libraries, artifacts, or other resources that help engineering teams apply these standards effectively.

What We’re Looking For

• Programming experience in Python, Rust, C++, or Solidity
• Experience using Generative AI tooling such as Claude Code, Codex, Cursor, or similar Large Language Model (LLM) tools.
• Pursuing a degree in Computer Science, Cybersecurity, or a related technical field
• General knowledge of the cryptocurrency ecosystem
• Interest in product development, secure engineering, and cybersecurity best practices
• Strong communication, problem-solving, and collaboration skills

Eligibility Requirements

• Undergraduate or master’s students graduating between December 2026 and June 2027
• Eligible master’s students must have fewer than two years of relevant, full-time work experience
• Students enrolled in PhD, MBA, or JD programs are not eligible to apply

Program Dates: Fall Semester

Location: Remote UK / Remote US

Compensation: $55/hr.

Apply now and join us on our mission to engineer a new economic paradigm.

Galaxy respects diversity and seeks to provide equal employment opportunities to all employees and job applicants for employment without regard to actual or perceived age, race, color, creed, religion, sex or gender (including pregnancy, childbirth, lactation and related medical conditions), gender identity or gender expression (including transgender status), sexual orientation, marital or partnership or caregiver status, ancestry, national origin, citizenship status, disability, military or veteran status, protected medical condition as defined by applicable state or local law, genetic information or predisposing genetic characteristic, or other characteristic protected by applicable federal, state, or local laws and ordinances.

We will endeavor to make a reasonable accommodation to the known limitations of a qualified applicant with a disability unless the accommodation would impose an undue hardship on the operation of our business. If you believe you require such assistance to complete the application process or to participate in an interview, please contact careers@galaxy.com.

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 126 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com.

Team overview:

The Engagement Modeling Team at Reddit focuses on building machine learning models to drive on-platform user engagement with diverse media and content, with a focus on predictive modeling to improve interactions of click-throughs and video view-throughs. This role offers a unique opportunity to shape and scale Reddit’s Ads prediction models, in alignment with our product goals and driving SoTA modeling advancement.

This role is well-suited for a leader with deep machine learning expertise, strategic vision, and a collaborative mindset to engage with both technical and cross-functional stakeholders.

We’re a remote-friendly company, and this position is open to candidates anywhere in the U.S.

Responsibilities:

Set Technical Vision and Strategy: Define and execute a roadmap for engagement modeling, balancing innovative modeling approaches with business objectives.
Drive Technical Execution: Oversee the model development lifecycle from ideation to deployment, ensuring high standards of ML performance and robustness.
Lead and Mentor a High-Performing Team: Recruit, mentor, and retain top ML talent, fostering a culture of growth, collaboration, and technical excellence.
Collaborate Cross-Functionally: Partner with PMs, data scientists, and other engineering teams to align on engagement strategies, data requirements, and model KPIs.
Innovate in ML Architecture: Implement and optimize model architectures tailored to engagement prediction, leveraging deep learning and advanced ML techniques.

Candidate Profile:

The EM will lead a diverse, high-impact team and will need to navigate and foster collaboration with various teams such as PM, DS, and engineering functions within Ads. Ideal candidates will have:

People Management Experience: Prior experience managing engineering teams with a strong emphasis on technical mentorship and team growth.
Set Technical Vision and Strategy: Ability to plan and execute a long-term technical strategy aligned with business objectives. Define and execute a roadmap for conversion modeling, balancing innovative modeling approaches with business objectives.
Drive Technical Execution: Oversee the model development lifecycle from ideation to deployment, ensuring high standards of ML performance and robustness.
Lead and Mentor a High-Performing Team: Recruit, mentor, and retain top ML talent, fostering a culture of growth, collaboration, and technical excellence.
Collaborate Cross-Functionally: Partner with PMs, data scientists, and other engineering teams to align on engagement strategies, data requirements, and model KPIs.
Innovate in ML Architecture: Implement and optimize model architectures tailored to conversion prediction, leveraging deep learning and advanced ML techniques.

Required qualifications:

At least 2+ of experience building and managing high-performing machine learning teams, ideally in the Ads domain. Will consider tech lead experience as well
Deep ML Expertise: Deep hands-on experience working with machine learning models and deploying them in large-scale production systems. Proven ability in training, evaluating, and deploying large-scale models.
End-to-End ML Lifecycle Experience: Proven ability in training, evaluating, and deploying large-scale models.
4+ years of hands-on experience with TensorFlow or PyTorch.
Strategic Thinking: Ability to develop and communicate a clear, compelling technical strategy that supports broader company objectives and addresses the needs of internal customers.
Impact-Driven Mindset: Passion for developing scalable, well-designed, and responsible AI solutions that drive business value.
Exceptional Communication & Collaboration: Strong interpersonal skills and a collaborative mindset, with the ability to effectively communicate complex technical topics to diverse audiences and build strong relationships with cross-functional partners 
Experience with Ads or Engagement Modeling is a plus.

Perks and Benefits:

100% remote opportunity (we have 4 office locations for hybrid/onsite work preference in NY, SF, LA and Chicago)
Competitive salary and equity options
Comprehensive health benefits (medical, dental, vision) & workplace perks (home office set up stipend etc)
Generous 401k matching
Flexible vacation policy 
Paid parental leave (4+ months)
Family planning support
Paid volunteer time off

#LI-AS1

Pay Transparency:

This job posting may span more than one career level.

In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/.

To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.

The base salary range for this position is:
$230,000—$322,000 USD

In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.

During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable. We will not sell your personal information or disclose it to any third party for their marketing purposes. We will delete any recording of your interview promptly after making a hiring decision. For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors.

Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve. Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.