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Strava

Staff AI Engineer

Strava is hiring a Staff AI Engineer. It pays $260k-$280k a year and Level rates it ; you can apply on Strava.

Level's read on this job

  • On Level's scale, this AI engineer role is Builds AI ●●●●: the job is building AI systems.
  • The listed pay starts 41% above the typical AI engineer posting on Level.
  • The posting allows some remote work.
  • Tools and skills the posting cites include LangChain, LLMs and Vector Search.
  • Level lists 27 other open roles at Strava.

AI in this role

Build shared GenAI platforms, LLM orchestration tools, and retrieval infrastructure enabling product teams to ship AI features at scale.

langchainllmsvector-searchragpython
ragllm-integrationai-agentsfine-tuningai-evaluationai-safetyai-engineeringplatform-engineeringorchestrationevaluation

About Strava

Strava is the app for active people. With over 200 million athletes in more than 185 countries, it’s more than tracking workouts—it’s where people make progress together, from new habits to new personal bests. No matter your sport or how you track it, Strava’s got you covered. Find your crew, crush your goals, and make every effort count. Start your journey with Strava today.

Our mission is simple: to motivate people to live their best active lives. We believe in the power of movement to connect and drive people forward.

We are looking for a Staff AI Engineer to join the GenAI + Discovery Platform team at Strava, a team at the core of Strava's AI strategy, responsible for building the shared tooling that enables product teams to ship high value GenAI-powered features at scale. This role sits at the intersection of AI engineering, platform engineering, and server engineering. You will own the systems that make it easy and reliable for all of our product teams to build user facing features on top LLMs at Strava, from shared context, tool management, agent loops, orchestration, data access, search and retrieval to evaluation and ROI frameworks. This is a high-leverage technical role: you're not just building infrastructure, you're building the core understanding of athletes that enable consistent athlete experiences, insight across product surfaces. You'll work closely with product engineers, product managers and data teams to translate cutting-edge AI capabilities into production-ready platforms that facilitate development of AI features that provide value to our athletes.

We follow a flexible hybrid model that translates to more than half your time on-site in our San Francisco office — three days per week.

What You'll Do:

  • 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.

You Will Be Successful Here By:

  • 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.

What You'll Bring to the Team:

  • 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.

For information on benefits, please click here.

Why Join Us?

Movement brings us together. At Strava, we’re building the world’s largest community of active people, helping them stay motivated and achieve their goals.

Our global team is passionate about making movement fun, meaningful, and accessible to everyone. Whether you’re shaping the technology, growing our community, or driving innovation, your work at Strava makes an impact.

When you join Strava, you’re not just joining a company—you’re joining a movement. If you’re ready to bring your energy, ideas, and drive, let’s build something incredible together.

Strava builds software that makes the best part of our athletes’ days even better. Just as we’re deeply committed to unlocking their potential, we’re dedicated to providing a world-class, inclusive workplace where our employees can grow and thrive, too. We’re backed by Sequoia Capital, TCV, Madrone Partners and Jackson Square Ventures, and we’re expanding in order to exceed the needs of our growing community of global athletes. Our culture reflects our community. We are continuously striving to hire and engage teammates from all backgrounds, experiences and perspectives because we know we are a stronger team together.

Strava is an equal opportunity employer. In keeping with the values of Strava, we make all employment decisions including hiring, evaluation, termination, promotional and training opportunities, without regard to race, religion, color, sex, age, national origin, ancestry, sexual orientation, physical handicap, mental disability, medical condition, disability, gender or identity or expression, pregnancy or pregnancy-related condition, marital status, height and/or weight.

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

California Consumer Protection Act Applicant Notice

How we rate this

Staff AI Engineer at Strava rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGLLM IntegrationAI agentsFine-tuningAI EvaluationAI SafetyAI EngineeringPlatform Engineering

Also common in AI engineer jobs at this level

Pay

This job starts at
$260k
Typical AI engineer job
$184k

This posting starts 41% above the typical AI engineer job. See AI engineer jobs

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. How have you integrated a large language model into a production application?
  3. How do you decide when an AI agent can act on its own versus asking for approval first?
  4. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  5. How do you decide that one model's output is better than another's for a given task?

Adapt your resume

  • List these exact terms on your resume: RAG, LLM Integration, AI agents, Fine-tuning, and AI Evaluation. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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