Job Description
As a Staff Backend Engineer at Raya, you will be the technical architect and hands-on builder for our recommendation ecosystem. You’ll build and evolve sophisticated, multi-stage retrieval and ranking systems, bridging applied ML/AI with production backend engineering to deliver algorithms that are both performant and intelligent.
You will join at a pivotal moment as we scale our recommendation systems to support growth and increasingly complex marketplace dynamics.
Responsibilities
- Architectural Leadership: Own the end-to-end architecture of Raya’s recommendation services while remaining deeply hands-on in implementation.
- Hands-on Implementation: Design and ship systems that handle cold-start problems, real-time user signals, exposure balancing, and large-scale feature lookups.
- System Evolution: Evolve our ranking systems toward scalable multi-stage architectures, including embedding-based retrieval and graph-aware ranking where appropriate.
- Cross-Functional Influence: Act as the primary technical liaison between Data Science, Product, and Infrastructure. Translate complex algorithmic requirements into scalable backend services.
- Mentorship & Excellence: Elevate the engineering bar across the organization. Conduct deep-dive design reviews, establishing best practice standards for backend patterns, and mentor Senior Engineers in recommender systems best practices.
- Operational Stewardship: Ensure the reliability of mission-critical recommendation loops. Optimize for low-latency inference and high-availability, even during peak global traffic.
- Ambiguity & Tradeoffs: Operate in evolving problem spaces where objectives must balance short-term engagement, long-term retention, and marketplace health.
- Experimentation: Partner with Product/Data Science to implement offline + online experiments.
Qualifications
- Education: Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent real-world expertise building and operating production recommendation or ranking systems.
- Experience: 8+ years of software development experience, with at least 3 years focused specifically on Recommender Systems in a production environment.
- RecSys Mastery: Deep practical experience with recommender approaches like collaborative filtering, content-based filtering, and hybrid models. Experience with two-stage architectures (Candidate Generation & Ranking).
- Infrastructure Skills: Expert-level proficiency in Golang, Node.js, or Python. Experience building or operating high-throughput discovery, search, or recommendation systems in production.
- Data Fluency: Advanced knowledge of Postgres, MongoDB, and ElasticSearch/OpenSearch, specifically regarding performance tuning for high-concurrency discovery features.
- System Design: A history of shipping platforms that have scaled to millions of users. You should be comfortable discussing the trade-offs between consistency, availability, and latency.
- A/B Testing: Experience designing and implementing A/B tests in marketplace or interference-prone environments.
What Sets You Apart
- Marketplace Intuition: You understand that ranking people is fundamentally different from ranking content. You’ve worked in environments (dating, social, marketplaces, ride-sharing) where exposure affects behavior, and you design with fairness, liquidity, and user perception in mind.
- The “Product Engineer” Mindset: You bring strong product judgment to technical decisions, protecting serendipity, privacy, and user trust while shipping measurable improvements.
- Systems Builder: You build durable internal abstractions, tooling, and documentation that make future iteration faster and safer.
- Algorithmic Intuition: You understand the math behind ranking models and can identify bias, feedback loops, and unintended system behaviors before they become production issues.
- Strategic Pragmatism: You optimize for shipping measurable impact over technical novelty. You know when to apply a simple heuristic and when to deploy a complex model.
- Bias Toward Shipping: You build quickly, learn from production signals, and iterate with discipline rather than over-optimizing prematurely.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
