- Company Name
- Strava
- Job Title
- Senior Engineering Manager - Applied Machine Learning
- Job Description
-
**Job Title**
Senior Engineering Manager – Applied Machine Learning
**Role Summary**
Lead a high‑impact team of machine‑learning engineers to design, develop, and production‑grade AI systems that power core user experiences for a global consumer fitness product. Own the end‑to‑end ML strategy, from prototyping and experimentation to scalable deployment and operational excellence, while driving cross‑functional alignment and fostering a collaborative, inclusive team culture.
**Expectations**
- Deliver AI‑driven features that directly impact millions of users, balancing innovation with business value.
- Grow and mentor engineering talent, building a scalable, high‑performing ML organization.
- Set and execute a clear roadmap for ML initiatives across product surfaces.
- Maintain operational excellence: automated retraining, monitoring, feature logging, A/B testing, and scalable infrastructure.
- Influence product and technology directions through strategic partnership with Product, Engineering, UX, Data, and Ops teams.
**Key Responsibilities**
- **Team Leadership** – hire, coach, evaluate, and develop engineering talent; cultivate inclusive culture.
- **ML Strategy & Roadmap** – define priorities, timelines, and success metrics for AI projects.
- **Model Lifecycle Management** – prototype, experiment, deploy, scale, and monitor models (personalization, recommendation, search, trust & safety).
- **Production Ops Excellence** – implement automated retraining pipelines, performance monitoring, feature logging, A/B testing frameworks, and scalable ML architecture.
- **Cross‑Functional Collaboration** – work with Product, Design, Data Science, Backend, and Platform teams to align on metrics, data pipelines, and release cadence.
- **Innovation & Problem Solving** – guide the team in developing novel algorithms for fitness‑specific challenges (e.g., activity prediction, athlete insights).
- **Stakeholder Communication** – present technical progress, risks, and trade‑offs to executive and cross‑functional stakeholders.
**Required Skills**
- 3+ years managing a full‑stack ML engineering team.
- Proven experience delivering production ML systems at scale, especially in recommendation, personalization, or user‑understanding domains.
- Strong understanding of ML ops best practices: automated retraining, model monitoring, feature logging, A/B testing, and scalable infrastructure (cloud, containers, orchestration).
- Proficiency in Python and major ML libraries (TensorFlow, PyTorch, scikit‑learn) and experience with ML frameworks/platforms (e.g., SageMaker, Vertex AI).
- Excellent communication, presentation, and stakeholder‑management skills.
- Leadership skills: coaching, mentorship, hiring, performance management, culture building.
**Required Education & Certifications**
- Bachelor’s or higher degree in Computer Science, Engineering, Machine Learning, Statistics, or related field (advanced degree preferred).
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San francisco, United states
On site
Senior
23-12-2025