About the Platform Engineer Role at Strava

This is a GenAI/ML Platform Engineer role on Strava's growing AI team, the group that uses machine learning, generative AI, and search to power personalization, recommendations, and trust and safety for over 200 million athletes. You will own the underlying platform that lets data scientists and engineers build sophisticated models and use large language models with less friction, and you will make sure those AI/ML features can be served reliably and performantly at Strava's scale. This is a build-from-the-ground-up, champion-the-platform role: end-to-end ownership from gathering stakeholder needs through development, adoption, and improvement.

Strava wants someone who has solved platform level problems, treats internal engineers and data scientists as customers, and has hands-on experience across the modern GenAI and MLOps stack. Expect the interview to probe both your systems and platform engineering depth and your ability to drive impact across teams. Note the posting lists the location as Remote TX but also describes a flexible hybrid model with three days per week on-site in San Francisco, so clarify location expectations early with your recruiter.

Quick Stats

* Typical process: 4 to 5 rounds, roughly 3 to 5 weeks end to end

* Format: Recruiter phone screen, then remote video technical and behavioral rounds, often a virtual or on-site final loop

* Core focus: Platform and systems design, MLOps tooling, GenAI technologies, production ML at scale, cross-team collaboration

* Difficulty: Hard, because you must combine deep platform engineering, production ML operational practices, and stakeholder-driven ownership in one loop

What Strava Looks For

* Experience solving platform level problems by building tools and systems that address real user (engineer and data scientist) challenges

* Hands-on work with GenAI technologies like LangChain, MCP servers, evaluation frameworks, and vector stores

* Fluency across MLOps tools like FastAPI, MLflow, Kubeflow, and Feast, plus shipping software in production at scale in cloud environments like AWS

* Strong interpersonal and communication skills with a collaborative, end-to-end ownership mindset

Round 1: Recruiter Screen (~30 minutes)

What to Expect

A recruiter will walk you through the role, the AI team's mission, and logistics like location, compensation ($155K to $175K plus equity), and the hybrid vs. remote expectation. They will confirm your background against the core requirements: platform level work, GenAI and MLOps experience, and production software at scale. Expect a quick motivation check on why Strava and why an AI platform role specifically.

Example Questions

* "Why are you interested in Strava and in a GenAI/ML platform role specifically?"

* "Walk me through your experience building platforms or internal tooling for engineers and data scientists."

* "Which MLOps and GenAI tools have you worked with most recently?"

* "What are your location expectations, given the San Francisco hybrid model?"

Tips

* Have a crisp two-minute pitch that connects your platform experience to Strava's mission of motivating people to live active lives.

* Get clarity early on the Remote TX vs. three-days-on-site-in-SF discrepancy so there are no surprises later.

* Rehearse this quick pitch in Nora's Standard Mode so your motivation and background summary land naturally under time pressure.

Round 2: Technical Screen (~60 minutes)

What to Expect

A hands-on technical round with an engineer, likely a mix of coding and practical platform questions. Expect problems that reflect real platform work: writing clean, production-quality code, building or extending an API or service, and reasoning about data handling. Because the role touches Strava's large fitness and geo datasets, be ready for questions involving data munging with tools like Pandas, Spark, SQL, or Airflow.

Example Questions

* "Design and implement a service endpoint (for example with FastAPI) that serves model predictions and handles errors gracefully."

* "How would you process and clean a large dataset of user activity records efficiently?"

* "Walk through how you would debug latency in a model-serving path in production."

* "Write code to log features consistently at both training and inference time."

Tips

* Think out loud about tradeoffs (throughput, latency, cost) since platform work is judged on judgment as much as code.

* Tie your answers to real tools named in the posting (FastAPI, MLflow, Pandas, Spark, Airflow) when relevant.

* Practice coding and system explanation aloud in Nora's Technical Mode to sharpen how you narrate design decisions in real time.

Round 3: Platform and ML Systems Design (~60 minutes)

What to Expect

The signature round for this role. You will design an end-to-end AI/ML platform component: think feature store, model retraining pipeline, model-serving infrastructure, or a GenAI application layer using LLMs. Expect to reason about scale (tens of millions of active users), reliability, and operational excellence including automated retraining, performance monitoring, feature logging, and A/B testing. GenAI-specific design (vector stores, evaluation frameworks, LangChain, MCP servers) is very likely given the team's focus.

Example Questions

* "Design a platform that lets data scientists deploy and retrain models with minimal friction. What components do you build?"

* "How would you architect a retrieval-augmented feature using a vector store and an LLM at Strava's scale?"

* "How do you design monitoring and A/B testing so teams can trust a newly deployed model in production?"

* "A partner team needs feature logging that is consistent across training and serving. How do you build that into the platform?"

Tips

* Frame every design around your internal customers (engineers and data scientists) and how you reduce their friction, which is exactly how the posting defines success.

* Cover the operational lifecycle explicitly: retraining, monitoring, feature logging, and A/B testing, not just the happy path.

* Run a full design walkthrough in Nora's Technical Mode to practice structuring a system from requirements to tradeoffs to rollout.

Round 4: Behavioral and Ownership (~45 minutes)

What to Expect

Usually led by the hiring manager, this round tests the "success" traits the posting emphasizes: leading as an owner, holding empathy for your engineer and data scientist customers, collaborating across teams, and driving innovation with a platform mindset. Expect STAR-style questions about projects you owned end to end, times you influenced partner teams, and how you champion adoption of tools you build.

Example Questions

* "Tell me about a platform or tool you owned end to end, from gathering needs to driving adoption."

* "Describe a time you had to influence engineers or data scientists to adopt something you built."

* "Tell me about a time you introduced a new technique or technology and had to gain buy-in for it."

* "Describe a project that did not go as planned. How did you stay accountable for the outcome?"

Tips

* Prepare stories that show ownership through adoption, not just shipping code, since the posting stresses following through to adoption and improvement.

* Highlight cross-functional collaboration and empathy for internal customers in at least one story.

* Rehearse these in Nora's Behavioral Mode to tighten your STAR structure and keep answers focused on measurable impact.

Round 5: Final Loop and Offer (~varies)

What to Expect

A final loop that may combine an additional technical or cross-functional interview with a values and culture conversation, followed by the offer stage. Strava emphasizes an inclusive, collaborative culture, so expect at least one conversation about how you work with others and contribute positively to the team. Once the loop clears, the recruiter will discuss compensation within the $155K to $175K range plus equity.

Example Questions

* "How do you balance shipping fast with building platform foundations that last?"

* "Tell me about a time you disagreed with a partner team and how you resolved it."

* "What excites you about working at the intersection of AI and fitness for a consumer product?"

* "How do you contribute to an inclusive and collaborative team culture?"

Tips

* Research the posted compensation and benefits so you can negotiate from an informed position rather than reacting on the spot.

* Anchor your value on scarce skills the posting names (GenAI plus MLOps plus production scale) to justify the top of the range.

* Practice the offer conversation in Nora's Salary Negotiation Mode so you can discuss base, equity, and expectations confidently without underselling yourself.

Frequently Asked Questions (FAQ)

1) How many rounds are there?

Typically 4 to 5 rounds: a recruiter screen, a technical screen, a platform and ML systems design round, a behavioral and ownership round, and a final loop plus offer. Exact structure varies, so confirm the loop with your recruiter. This is our best estimate based on the posting and how companies at Strava's stage hire for AI platform roles, not a published process.

2) What topics are most common?

* Platform and ML systems design (feature stores, model serving, retraining pipelines, GenAI application layers)

* MLOps and GenAI tooling (FastAPI, MLflow, Kubeflow, Feast, LangChain, MCP servers, vector stores), production ML at scale, and end-to-end ownership stories

3) How long does the process take?

Usually around 3 to 5 weeks from recruiter screen to offer, depending on scheduling and the size of the final loop.

4) How should I prepare?

* Study the modern GenAI and MLOps stack named in the posting and be ready to design end-to-end platform components at Strava's scale.

* Prepare operational-excellence answers covering automated retraining, performance monitoring, feature logging, and A/B testing.

* Build STAR stories that show end-to-end ownership, cross-team influence, and empathy for internal engineer and data scientist customers.

* Practice with Nora: use Technical Mode for coding and systems design, Behavioral Mode for ownership and collaboration stories, Standard Mode for the recruiter pitch, and Salary Negotiation Mode for the offer conversation.