Staff AI Engineer

Strava•San Francisco, CA

2d•$260,000 - $280,000•Hybrid

About The Position

Strava is seeking a Staff AI Engineer to join the GenAI + Discovery Platform team. This role is central to Strava's AI strategy, focusing on building shared tooling that empowers product teams to deploy GenAI-powered features at scale. The position involves AI engineering, platform engineering, and server engineering, with a focus on owning systems that simplify the development of user-facing features on top of LLMs. Responsibilities include managing shared context, tool management, agent loops, orchestration, data access, search and retrieval, and evaluation frameworks. This is a high-leverage technical role aimed at building the core understanding of athletes to ensure consistent athlete experiences and insights across product surfaces. The engineer will collaborate with product engineers, product managers, and data teams to translate AI capabilities into production-ready platforms for AI feature development that benefit athletes.

Requirements

5+ years of experience building and operating complex, production AI or backend systems at scale, with a track record of decomposing large technical problems into well-scoped execution across teams.
Demonstrated experience building AI platform tooling or developer-facing infrastructure ideally for LLM or ML systems with a strong instinct for API design, versioning, and self-serve patterns.
Hands-on experience building with large language models in production: agentic workflows, prompt and context engineering, RAG architectures, embedding pipelines, fine-tuning workflows, or LLM evaluation frameworks and tools like Langchain .
Proficiency in search systems (Elasticsearch/OpenSearch, Vector Search, etc)
Proficiency in backend service development on cloud environments (AWS preferred), using Python, Go. Solid understanding of distributed systems and containerized infrastructure (Kubernetes, Docker).
Strong technical leadership: ability to lead multi-team projects, define technical direction, and grow engineers at multiple levels.
Deep curiosity about the evolving GenAI landscape: model capabilities, agent frameworks, multimodal systems: and strong judgment on where and how to apply them to real product problems.
Strong communication and collaboration skills, with the ability to align cross-functional partners around technical direction and build organizational trust in the platforms your team ships.

Nice To Haves

Treating AI Platform as a Product: Bringing engineering rigor — versioning, contracts, SLAs, monitoring, and deprecation paths — to AI capabilities and LLM integrations that product teams depend on. You don't ship a prototype; you ship a platform.
Leading as an Owner: Taking end-to-end accountability for the reliability and impact of the systems you build, including their correctness in production, their adoption by downstream teams, and the business outcomes they enable.
Building for Leverage: Designing platforms and tooling that multiply the output of the broader team, reducing the AI infrastructure expertise required for CUJ teams to ship GenAI-powered features.
Collaborating Across Disciplines: Working fluidly with ML engineers, data engineers, data scientists, and product managers to align on model selection, evaluation standards, prompt strategies, and consumption patterns.
Raising the Standard: Helping establish best practices for GenAI system development, responsible AI patterns, and operational health, and mentoring teammates at all levels to do the same.
Being passionate about the work you are doing and contributing positively to Strava's inclusive and collaborative team culture and values.

Responsibilities

Build for a Well Loved Consumer Product: Work at the intersection of AI and fitness to launch and optimize product experiences that will be used by tens of millions of active people worldwide.
Build the GenAI + Discovery Platform: Set the vision, Design and the shared genAI platform: LLM, and workflow orchestration, prompt management systems, RAG pipelines, search and retrieval services (vector, hybrid and structured search), and evaluation tooling
Enable Teams to Ship AI Features Faster: Build self-serve interfaces and golden paths so that product and CUJ engineering teams can build GenAI-powered features without deep AI expertise.
Own End-to-End AI Capability Delivery: Drive projects from architecture and interface design through production deployment and monitoring, ensuring correctness, latency, reliability, and cost-efficiency of the AI capabilities your platform serves.
Collaborate Across Engineering, and Product: Work closely with engineers and PMs across different verticals to enable the new features and build the genAI roadmap. inform product teams on how to consume and leverage AI capabilities effectively.
Build from a Rich Dataset: Explore and use Strava's extensive unique fitness and geo datasets from millions of users to inform how AI capabilities can extract actionable insights, improve product decisions, and power novel athlete experiences.