Reddit, Inc.

Machine Learning Engineer

• Define the technical vision and long-term roadmap for Feed Retrieval, aligning large-scale recommender-system investments with Reddit’s product, ecosystem, and business objectives.
• Translate broad Feed Relevance goals into a focused team roadmap, making clear prioritization tradeoffs across model quality, inventory expansion, experimentation velocity, infrastructure cost, and operational reliability.
• Coach and support the development of your team, constantly seeking opportunities to grow their skills and impact.
• Oversee the design, development, and optimization of retrieval systems that source relevant, diverse, fresh, and high-quality candidates for personalized feed experiences.
• Establish strong measurement, experimentation, and debugging practices so the team can understand retrieval quality, candidate coverage, source incrementality, and downstream impact.
• Collaborate with ML platform, infrastructure, ranking, safety, and product teams to build scalable, low-latency retrieval systems that can support the next generation of AI-powered recommendations.
• Maintain high standards for system performance, reliability, latency, cost efficiency, and responsible recommendation practices.
• Work with cross-functional partners from across the company to identify key areas of opportunity, set expectations, and communicate your team’s work.
• Partner with our incredible recruiting team to attract, interview, and hire diverse and talented machine learning engineers, growing a world-class team.

Machine Learning Manager, Feed Relevance – Retrieval

• Define the technical vision and long-term roadmap for Feed Ecosystems.
• Coach and support the development of your team.
• Work closely with product, design, data science, safety, community, ads, and platform partners.
• Oversee the design, development, and optimization of ML systems.
• Help define and operationalize signals for subjective and objective quality.
• Collaborate with platform and infrastructure teams to build scalable AI-powered systems.
• Maintain high standards for system performance, reliability, efficiency, and responsible AI practices.
• Partner with our recruiting team to attract, interview, and hire diverse and talented machine learning engineers.

Machine Learning Manager, Feed Ecosystems

Engineering Manager

• Lead, hire, onboard, and develop a high-caliber team of infrastructure engineers across Transport and Site Defense.
• Partner with senior ICs to define technical strategy for global traffic, edge/CDN, service discovery, gRPC datapath, rate limiting, site defense, and bot detection infrastructure.
• Drive multi-quarter execution on high-risk platform migrations, including CDN migration, inter-cluster load balancer adoption, Thrift to GRPC migration, and Site Defense Platform expansion.
• Establish clear priority frameworks that balance KTLO, roadmap commitments, incidents, business-critical migrations, and cross-functional asks.
• Build and maintain a healthy operational model for critical infrastructure, including on-call quality, SLOs, dashboards, incident response, postmortems, and proactive maintenance.
• Collaborate deeply with partner teams across Consumer, Ads, Media Platform, Graph QL, SEO, Web Platform, Info Sec, Compute, ML Platform, Storage, DevEx, Safety/Trust, CIAM, and Site Experience SRE.
• Coach engineers through technical design, stakeholder alignment, project planning, execution risk, communication, and career growth.
• Create a culture of metrics-driven quality, operational transparency, crisp ownership, and pragmatic technical ambition.

Engineering Manager, Site Defense

• Independently own high-value optimization initiatives across training, inference, or launch-readiness for important Ads ML workloads.
• Diagnose bottlenecks in real production systems using profiling, benchmarking, and observability rather than intuition-first debugging.
• Build performance tooling, optimization playbooks, observability hooks, guardrails, or efficiency primitives that help more than one team or workload over time.
• Improve launch-safety and efficiency readiness by contributing to load testing, fallback readiness, latency and cost visibility, and operational confidence for heavy models.
• Work with model owners and platform teams to land pragmatic fixes while helping the team gradually standardize repeated solutions.
• Contribute to the team’s technical direction by surfacing patterns, tradeoffs, and opportunities for reuse or automation.
• Mentor less-experienced engineers through code, debugging, measurement rigor, and strong execution habits.

Senior Machine Learning Engineer, ML Efficiency

Product Adoption Specialist

• Lead efforts to scale the adoption of Reddit’s priority ads products, increasing advertiser engagement and revenue.
• Develop and manage programs to support product adoption across Large Customer Sales (LCS) advertisers and sales teams in the United States.
• Work directly with advertisers to identify, pitch, activate, onboard, and grow clients with strategic product opportunities.
• Partner closely with Sales, Product, Product Marketing Management (PMM), Revenue Strategy &amp; Analytics teams, GTM Enablement, and more in alignment to strategic goals.
• Equip sales teams with the necessary knowledge, training, collateral, and market insights to effectively position and implement Reddit's ad products.
• Advocate for advertiser needs and identify opportunities and gaps, providing feedback to PMM and Product teams to inform ongoing product development.
• Partner with Sales and Strategy/Ops leadership to understand progress to goal and identify steps that can be taken to accelerate outcomes.

Senior Product Adoption Strategist, Shopping

Frontend Engineer

• Own the technical design and delivery of complex frontend features across Ads Manager’s creative and recommendation workflows.
• Translate AI-backed capabilities into interfaces that advertisers can understand, steer, and refine.
• Partner with product and design to work through ambiguous requirements and make sound product and engineering tradeoffs.
• Develop reusable frontend patterns and components that support a unified campaign creation experience.
• Instrument experiences, evaluate results, and use experiments and product data to guide iteration.
• Raise the quality of the codebase through thoughtful architecture, testing, observability, performance work, and accessibility.
• Provide high-quality code reviews and technical guidance, and help other engineers grow.
• Collaborate across frontend, backend, data, and machine learning systems to deliver reliable end-to-end experiences.

Senior Frontend Engineer, Ads Creative

• Design, develop, optimize and deploy ML models, including large language models, for various natural language processing tasks.
• Implement and maintain training pipelines, leveraging distributed training and optimizing for performance and efficiency.
• Collaborate with cross-functional teams to gather requirements, define model architectures and iterate on model development.
• Conduct model evaluations, performance analysis, and optimization to improve model accuracy and reduce biases.
• Stay up-to-date with the latest research and advancements in the field of natural language processing, multimodal signals, and large language models.
• Contribute to the development of best practices, guidelines, and ethical AI principles for responsible ML development and deployment.

Senior Machine Learning Engineer, Safety

Data Scientist

• Design the Future of Ads Identity: Develop/employ probabilistic models for identity resolution. Design the methodology that links on-platform and off-platform actions to maximize addressability while honoring privacy.
• Advance Lift Methodologies & Experimentation: Own the statistical rigor behind Reddit’s Brand and Conversion Lift products. Innovate experimental design and develop infrastructure that supports large-scale, high-velocity, low-bias testing for advertisers.
• Maximize Signal for Predictive Performance: Define the strategy for new signal sources. You will mathematically quantify the value of these signals and work with modeling teams to incorporate them into predictive models, directly improving bidding efficiency and ROAS.
• Define Ground Truth & Evaluation Frameworks: Solve the industry-wide challenge of validating identity and measurement. Design the objective functions and truth sets used to train our models and measure the incremental impact of our identity graph.
• Lead Through Cross-Functional and Technical Influence: Collaborate deeply with engineering, product, and sales to align on strategic goals, translate insights into action, and drive execution. Set a high technical bar by mentoring others and championing best practices across modeling, experimentation, and measurement.

Staff Data Scientist – Ads Measurement, Signals, Privacy

• Define the long-term data science vision & strategy across ads measurement, signal quality, identity, attribution, and privacy. Establish how Reddit should evaluate advertiser value, measurement quality, and signal utility across first-party and third-party products.
• Define the long-term data science strategy across ads measurement, signal quality, identity, attribution, and privacy. Establish how Reddit should evaluate advertiser value, measurement quality, and signal utility across first-party and third-party products.
• Create rigorous frameworks for validating lift, attribution, identity quality, modeled conversions, signal loss recovery, and privacy-aware measurement. Define ground truth, objective functions, quality metrics, guardrails, and decision frameworks that guide product and engineering investments.
• Lead the evolution of Reddit’s experimentation and lift methodologies across Brand Lift, Conversion Lift, Split Testing, and emerging measurement products. Improve study quality, reduce bias and contamination, and develop scalable diagnostics for experiment health, feasibility, and interpretability. Partner with Ads Engineering to operationalize complex causal models, ensuring that scientific methodologies are not only accurate but also performant, scalable, and resilient in high-throughput production environments.
• Quantify how signal quality, match rates, identity resolution, modeled conversions, and privacy changes affect bidding efficiency, CPA, ROAS, and advertiser outcomes. Partner with modeling and ranking teams to translate measurement improvements into performance gains.
• Lead a privacy-first measurement paradigm, positioning Reddit as a market leader in trusted advertising by recovering signal utility through compliant modeling.
• Lead cross-org efforts to define reusable methodologies, dashboards, scorecards, quality metrics, and best practices. Build repeatable systems that improve how Ads DS evaluates launches, monitors regressions, sizes opportunities, and communicates impact.
• Partner with senior leaders across Product, Engineering, Sales, Marketing Science, Legal/Privacy, and Ads leadership to shape roadmap decisions. Translate complex scientific tradeoffs into clear business and product recommendations.
• Mentor Staff and Senior data scientists, sponsor high-impact technical work, and raise the bar for causal inference, measurement science, identity evaluation, data quality, and cross-functional decision-making across Ads DS.

Senior Data Scientist – Ads Measurement, Signals, Privacy

Product Manager

• Define the product strategy for Games Ecosystem, including how games fit into Reddit's communities, creator platform, and long-term growth strategy.
• Identify the highest-leverage opportunities across player experience, community participation, creator/developer enablement, game discovery, retention, and ecosystem quality.
• Build a roadmap that balances consumer-facing experiences, community-native features, creator/developer tools, platform capabilities, and ecosystem programs.
• Help communities become richer destinations for game-related participation, including discussion, identity, events, social play, creator content, and other repeatable engagement loops.
• Improve how high-quality games and game-adjacent experiences are discovered, distributed, and revisited across Reddit.
• Partner with Engineering, Design, Data, ML/ranking, DevRel, Community, Partnerships, Trust/Safety, Policy, and other teams to ship durable ecosystem improvements.
• Work with creators, developers, studios, moderators, and community builders to understand what makes games successful on Reddit and where the platform is creating friction.
• Define the right measurement framework for player value, community value, builder success, experience quality, retention, discovery, and ecosystem health.
• Create clear investment frameworks that help leadership decide when to build natively, support creators/developers, partner externally, improve platform primitives, or stop lower-impact work.
• Lead cross-functional teams through ambiguity and help them move from broad strategy to concrete shipped product.

Staff Product Manager, Games Ecosystem

Full-stack Engineer

• Architect for Scale: Define and execute the org-wide client architecture strategy to support seamless video creation, editing, and uploading directly within our shared composers. Establish the patterns for how all future formats integrate into the client layer.
• Accelerate Product Roadmap: Build and scale new contribution formats natively within the shared post and comment experiences. Unblock execution for high-upside strategic bets designed to reduce identity-risk barriers for sensitive content and productize viral, low-effort creation formats that convert passive consumers into active contributors.
• Mentorship & Org Leadership: Serve as the technical anchor for the Contribution engineering team and influence the broader Content Ecosystem org. Translate complex product requirements into actionable technical roadmaps and drive alignment across Web, iOS, and Android platforms.
• Leverage Modern Tooling: Thoughtfully utilize AI-assisted development tools to accelerate prototyping, system design, and coding, while applying rigorous judgment to validate and own the final outputs. Evangelize AI as a tool.
• Optimize Contribution Funnel: Lead the engineering of highly performant client infrastructure. You will tackle challenges like zero-latency rich-text rendering, resilient edge-optimized media upload pipelines, and complex state synchronization for offline and online editing to maximize global contribution success rates.

Staff Software Engineer, Client Architecture

Backend Engineer

• Work cross-functionally with product, design, and other engineering counterparts to execute on product and business strategy and build novel products and features that our users will love.
• Contribute to the full development cycle: technical design, development, test, experimentation, analysis, and launch. You’ll be reviewing code and design docs, giving feedback on product specs and mocks.
• Continuously learn and improve your technical and non-technical abilities.

Backend Engineer – Moderation Enforcement

Mechanical Engineer

• Evolve Reddit’s cross-platform design system by expanding its capabilities and developing higher-level reusable components that accelerate product development while maintaining consistency and quality.
• Collaborate cross-functionally with product teams, designers, and engineers to ensure the design system and UI infrastructure effectively support real product needs.
• Improve performance at scale, contributing to Android architecture, rendering patterns, and performance-focused development practices that make performance a built-in feature of the platform.
• Advance accessibility across the platform, building accessible components, patterns, tooling, and documentation that help teams adopt accessibility best practices by default.
• Strengthen developer experience, contributing to shared tooling, documentation, and platform improvements that help engineers build reliable, consistent UI across Reddit’s products.

Senior Android Engineer, Design System

• Lead efforts to scale the adoption of Reddit's lower funnel ads products, increasing advertiser engagement and performance revenue.
• Develop and manage programs to support product adoption across Mid-Market (MM) advertisers and sales teams in North America.
• Identify critical insights, themes across your managed verticals and synthesize them into actionable recommendations for Sales, PMM, and PM.
• Work with top opportunity advertisers to activate and grow strategic product opportunities.
• Partner closely with Sales, Product Marketing Management (PMM), Revenue Strategy & Analytics (RSA), and Product teams to ensure seamless knowledge transfer and alignment of strategic goals.
• Equip sales teams with the necessary knowledge, training, collateral, and market insights to effectively position and implement Reddit's ad products.
• Advocate for advertiser needs and identify opportunities and gaps, providing feedback to PMM and Product teams to inform ongoing product development.
• Understand progress to goal and identify/execute steps to accelerate outcomes. Leverage automation tools to build reporting outputs and consistent reporting cadence to Sales leadership.