Machine Learning Engineer developing and optimizing AI systems at Strava. Work includes building innovative models that enhance fitness experiences for millions of users.
About the role
- Staff Machine Learning Engineer leading a team focused on content recommendations for Pinterest. Driving multi-quarter technical vision and hands-on ML systems design and execution.
Responsibilities
- Own the long‑term architecture, roadmap, and execution for source discovery, acquisition optimization, and content understanding.
- Lead design reviews, set engineering standards, and drive cross‑team alignment with Product, Data, and Infra.
- Mentor and uplevel MLEs through technical direction, pairing, and reviews.
- Train/fine‑tune LLMs and NLP models for classification, extraction, and instruction‑following; design eval loops and guardrails.
- Design features and frameworks for sharing features across models.
- Productionize models for large-scale inference; drive latency, reliability, and cost efficiency (quantization, distillation, caching).
- Establish offline/online evaluation, gold sets, and automated regressions; run A/B and canary/shadow launches.
- Work with human and automated labeling sources to define data labeling standards.
- Partner on data strategy, labeling/weak supervision, and feedback loops to expand coverage and improve precision/recall.
- Define and meet SLOs for data quality, model performance, and serving reliability; lead incident playbooks and postmortems.
- Measure and drive downstream impact on revenue and engagement.
Requirements
- 5+ years building ML products end‑to‑end, including 2+ years as a tech lead driving multi‑quarter roadmaps and cross‑functional execution.
- Deep hands‑on experience with NLP/LLM training and inference (PyTorch, Python); strong grounding in evaluation, prompt/data design, and fine‑tuning.
- Proven track record shipping models at scale: feature/data pipelines, online serving, monitoring/observability, and cost/perf trade‑offs.
- Strong software engineering in Python with an eye for software engineering best practices.
- Experience mentoring senior engineers and influencing partner teams.
- Masters or PhD in ML related studies.
- LLM efficiency techniques (LoRA/adapters, distillation, quantization, prompt caching) and cost control strategies.
- MLOps at scale with tools like Airflow, Spark/Presto, Triton, vLLM.
Benefits
- This position is not eligible for relocation assistance
- Visit our PinFlex page to learn more about our working model.
Job title
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Machine Learning Engineer developing AI systems for Strava's fitness platform. Working on personalized recommendations and optimizing user experiences for millions worldwide.
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