About the Role
Reddit is looking for a Staff Machine Learning Engineer to build the ranking and personalization systems that connect redditors with their next favorite game or app on Reddit. You will work closely with Product, Backend, Data Science, and Core Ranking/ML Platform teams to design and ship best-in-class ranking, retrieval, and experimentation systems that power discovery and re-engagement for Dev Platform experiences across feeds, surfaces, and notifications.
You will own problems end-to-end—from framing objectives and defining signals, to training and deploying models, to designing experiments and reading results—not just tuning existing knobs on mature systems. You’ll help define the ranking strategy for Developer Platform, stand up new ML models and feedback loops where none exist today, and turn noisy behavioral data into clear wins for users, creators, and developers.
This is a high-impact, 0→1 role where your work will directly shape how interactive apps and games show up on one of the largest sites in the world. You’ll set technical direction for Dev Platform ranking, raise the bar on relevance and system reliability, and mentor other engineers as we scale a team of builders who value impact, personal growth, openness, and kindness.
What You’ll Do
- Design and ship next-generation ranking and personalization models that power discovery of Dev Platform apps and games across Reddit surfaces.
- Partner with ML, DS, and product to define signals, APIs, and feedback loops for building, deploying, and iterating on ranking models.
- Explore and productionize new retrieval, ranking, and experimentation approaches that unlock new Dev Platform experiences.
- Champion model quality, reproducibility, and experimentation best practices — including offline evaluation, A/B testing, and monitoring for model health and degradation.
- Mentor engineers and lead technical discussions, shaping the long-term ranking and personalization modeling strategy for Dev Platform.
Who You Might Be
- 8+ years of experience building and deploying ML models in production, particularly in ranking or personalization modeling, large-scale representation learning, or recommender systems.
- Proven track record working on cross-functional product teams (PM, Design, DS, Eng) where you owned end-user outcomes, not just models or infra, and shipped features that moved core product metrics.
- Strong domain knowledge in modern ranking and personalization techniques: user/item embeddings, multi-task learning, sequential modeling, or similar approaches applied at scale.
- Experience designing and implementing performant, stable, and efficient ML or ranking systems (recommendation, ads, search, feed, or similar high-throughput decision systems).
- Strong organizational skills with the ability to prioritize, sequence, and de-risk work, keeping complex projects on schedule with a high attention to detail.
- BS in Computer Science or a related technical field, or equivalent practical experience.
- Comfortable with software engineering best practices: testing, code reviews, technical design docs, and clear documentation for other teams that depend on your systems.
- Entrepreneurial mindset: you are self-directed, comfortable in ambiguity, and biased toward action in fast-paced environments. You like 0→1 building, iteration, and learning from experiments and failures.
- Excellent communication skills: you collaborate effectively in a remote, cross-functional team, and can explain complex ML and ranking concepts to both technical and non-technical stakeholders.
Technical Skills
- Languages: Go, Python, C++ or any object oriented programming language
- Libraries: Baseplate, GraphQL
- Databases: Redis, Postgres, Memcached
- Tools: Kubernetes, Docker, AWS, GCP