Machine Learning Platform Engineer

📍 Job Overview

• Job Title: Senior Machine Learning Platform Engineer
• Company: Strava
• Location: San Francisco, CA
• Job Type: Hybrid (3 days on-site per week)
• Category: Machine Learning Engineer, Platform Engineer
• Date Posted: 2025-08-01
• Experience Level: 5-10 years

🚀 Role Summary

• 📝 Enhancement Note: This role focuses on developing and expanding Strava's machine learning platform, enabling faster model iteration and reliable deployment at scale. It involves working at the intersection of AI and fitness to enhance product experiences for millions of active users worldwide.

• Lead key projects to level up Strava's ML tools and systems, growing with use cases, model architectures, and athletes.

• Build for a well-loved consumer product, working with extensive unique fitness and geo datasets to extract actionable insights.

• Shape AI at Strava by being a strong voice and mentor on a collaborative team with varying experience levels.

• Collaborate across teams to deploy ML solutions across multiple surfaces and expand Strava's technical ML capabilities.

💻 Primary Responsibilities

• 📝 Enhancement Note: The primary responsibilities revolve around owning and leading projects to enhance Strava's machine learning platform, ensuring end-to-end system delivery, and driving innovation with a product focus.

• Own scalable platform: Lead key projects to level up Strava’s ML tools and system in a way that grows with our use cases, model architectures, and athletes.

• Build for a well-loved consumer product: Work at the intersection of AI and fitness to enable product experiences used by tens of millions of active people worldwide.

• Shape AI at Strava: Be a strong voice and mentor on a highly collaborative team with a range of experience levels. Work across teams to deploy ML solutions across multiple surfaces and expand our technical ML capabilities.

• Build from a rich dataset: Help us make use of Strava’s extensive unique fitness and geo datasets from millions of users to extract actionable insights, inform product decisions, and optimize existing features.

🎓 Skills & Qualifications

Education: Bachelor's degree in Computer Science, Statistics, Mathematics, or a related field. Advanced degree preferred.

Experience: 5-10 years of experience in machine learning, with a focus on platform engineering, MLOps, and production ML model deployment.

Required Skills:

• Proven experience in complex, ambiguous platform challenges and breaking them down into manageable tasks.
• Strong interpersonal and communication skills, with a collaborative approach to drive business impact across teams.
• Experience with various MLOps tools (e.g., FastAPI, LitServe, Metaflow, MLflow, Kubeflow, Feast).
• Proficiency in production ML model operational excellence and best practices, such as automated model retraining, performance monitoring, feature logging, and A/B testing.
• Experience building backend production services on cloud environments like AWS, using languages like Python, Terraform, and other similar technologies.
• Experience building, shipping, and supporting ML models in production at scale.
• Experience with exploratory data analysis and model prototyping, using languages such as Python or R and tools like Scikit learn, Pandas, NumPy, PyTorch, TensorFlow, and Sagemaker.

Preferred Skills:

• Experience with data pipelines using large-scale data technologies like Spark, SQL, and Snowflake.
• Familiarity with fitness and geo datasets.
• Knowledge of Strava's products and services.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

• Demonstrate experience in building and maintaining machine learning platforms at scale.
• Showcase projects that highlight your ability to work with large datasets, extract insights, and inform product decisions.
• Include examples of your work in production ML model deployment, performance monitoring, and A/B testing.

Technical Documentation:

• Provide clear and concise documentation of your code, including version control, deployment processes, and server configuration.
• Include examples of your work in model retraining, feature logging, and data pipeline development.

💵 Compensation & Benefits

Salary Range: $180,000 - $210,000 per year (base compensation only, does not include equity or benefits)

Benefits:

• Comprehensive health, dental, and vision insurance for employees and dependents.
• Flexible time off and paid holidays.
• 401(k) retirement plan with company matching.
• Employee stock options and equity compensation.
• Commuter benefits and wellness reimbursement.
• Professional development opportunities and tuition reimbursement.
• Employee assistance program and mental health resources.
• Parental leave and family planning support.

Working Hours: Full-time, 40 hours per week. Flexible hybrid work arrangement with approximately 3 days on-site in the San Francisco office.

🎯 Team & Company Context

🏢 Company Culture

Industry: Fitness technology and social networking.

Company Size: Medium (151-500 employees)

Founded: 2009

Team Structure:

• The AI and Machine Learning team is responsible for sophisticated machine learning models and systems that provide value to Strava athletes, including personalization, recommendations, search, and trust and safety.
• The team maintains the ML platform and infrastructure that enables quick model iteration and reliable deployment at scale.
• The team collaborates with various product verticals and partner teams to identify opportunities and bring technical visions to life.

Development Methodology:

• Strava follows a flexible hybrid work model, with approximately half of the time spent on-site in the San Francisco office.
• The team uses Agile methodologies, with a focus on continuous integration, delivery, and improvement.
• Strava emphasizes a data-driven approach to decision-making and prioritizes user experience and product innovation.

Company Website: Strava.com

📈 Career & Growth Analysis

Machine Learning Platform Engineer Career Level: This role is at the senior level, focusing on developing and expanding Strava's machine learning platform. It involves leading key projects, driving innovation, and mentoring team members.

Reporting Structure: This role reports directly to the Manager of Machine Learning Engineering.

Technical Impact: The Senior Machine Learning Platform Engineer has a significant impact on Strava's ability to deliver high-quality, personalized experiences to millions of active users worldwide. They play a crucial role in ensuring the scalability, reliability, and performance of Strava's ML platform and systems.

Growth Opportunities:

• Technical Growth: Expand your expertise in machine learning platform engineering, MLOps, and production ML model deployment. Stay up-to-date with the latest research and tools in the field.
• Leadership Development: Develop your leadership skills by mentoring team members, driving projects, and influencing the ML team and partner teams.
• Architecture Decisions: Contribute to strategic architecture decisions that shape Strava's ML platform and systems, enabling the company to scale and innovate.

🌐 Work Environment

Office Type: Hybrid, with approximately half of the time spent on-site in the San Francisco office.

Office Location(s): San Francisco, CA

Workspace Context:

• Strava's offices are designed to foster collaboration, creativity, and comfort, with a focus on open communication and teamwork.
• The workspace includes multiple monitors, testing devices, and development tools to support web development and machine learning tasks.
• Strava encourages a flexible and inclusive work environment that values diversity, equity, and belonging.

Work Schedule: Full-time, 40 hours per week. Flexible hybrid work arrangement with approximately 3 days on-site in the San Francisco office.

📄 Application & Technical Interview Process

Interview Process:

1. Phone Screen (30 minutes): A brief call to discuss your background, experience, and interest in the role.
2. Technical Phone Screen (60 minutes): A deeper dive into your technical skills, with a focus on machine learning platform engineering, MLOps, and production ML model deployment.
3. On-site Interview (4-5 hours): A series of interviews with team members, including a technical deep-dive, system design discussion, and cultural fit assessment.
4. Final Evaluation (30 minutes): A meeting with the hiring manager to discuss your fit for the role and next steps.

Portfolio Review Tips:

• Highlight your experience in building and maintaining machine learning platforms at scale.
• Showcase projects that demonstrate your ability to work with large datasets, extract insights, and inform product decisions.
• Include examples of your work in production ML model deployment, performance monitoring, and A/B testing.

Technical Challenge Preparation:

• Brush up on your machine learning platform engineering, MLOps, and production ML model deployment skills.
• Familiarize yourself with Strava's products, services, and technology stack.
• Prepare for system design discussions, focusing on scalability, reliability, and performance.

ATS Keywords: Machine Learning, MLOps, Platform Engineering, Production ML Model Deployment, Data Pipelines, Spark, SQL, Snowflake, Model Deployment, Performance Monitoring, Feature Logging, A/B Testing, Scikit Learn, Pandas, NumPy, PyTorch, TensorFlow, Sagemaker, AWS, Python, Terraform, Hybrid Work, Agile Methodologies, Data-Driven Decision Making, User Experience, Product Innovation.

🛠 Technology Stack & Web Infrastructure

Machine Learning Platform & Infrastructure:

• MLOps Tools: FastAPI, LitServe, Metaflow, MLflow, Kubeflow, Feast
• Cloud Environment: AWS
• Programming Languages: Python, Terraform
• Data Processing & Analysis: Spark, SQL, Snowflake, Pandas, NumPy
• Machine Learning Frameworks: Scikit learn, PyTorch, TensorFlow, Sagemaker
• Deployment & Monitoring: Kubernetes, Prometheus, Grafana

Strava's Technology Stack:

• Backend: Node.js, Express.js, Django, Flask
• Frontend: React, Redux, TypeScript, Webpack
• Databases: PostgreSQL, MongoDB, Redis, Elasticsearch
• Search: Elasticsearch, Algolia
• Messaging: RabbitMQ, Apache Kafka, AWS SQS
• Storage: Amazon S3, Google Cloud Storage, AWS RDS, MongoDB Atlas
• Monitoring & Logging: Datadog, New Relic, ELK Stack
• Infrastructure as Code: Terraform, AWS CloudFormation, Docker
• CI/CD: Jenkins, CircleCI, GitHub Actions
• Version Control: Git, GitHub

👥 Team Culture & Values

Strava's Core Values:

• Move: We believe in the power of movement to connect and drive people forward.
• Connect: We build products that bring people together and make them feel part of something bigger.
• Empower: We empower our employees to make decisions, take risks, and grow both personally and professionally.
• Inclusive: We foster an inclusive environment where everyone feels valued and respected.
• Curious: We embrace curiosity and continuous learning to stay at the forefront of our industry.

Collaboration Style:

• Cross-Functional Integration: Strava encourages collaboration across teams, with a focus on open communication, active listening, and collective problem-solving.
• Code Review Culture: Strava values code reviews as a means of knowledge sharing, learning, and maintaining high coding standards.
• Knowledge Sharing: Strava fosters a culture of knowledge sharing, with regular team meetings, workshops, and brown bag sessions to help employees grow and develop their skills.

🌐 Work Environment

Office Type: Hybrid, with approximately half of the time spent on-site in the San Francisco office.

Office Location(s): San Francisco, CA

Workspace Context:

• Strava's offices are designed to foster collaboration, creativity, and comfort, with a focus on open communication and teamwork.
• The workspace includes multiple monitors, testing devices, and development tools to support web development and machine learning tasks.
• Strava encourages a flexible and inclusive work environment that values diversity, equity, and belonging.

Work Schedule: Full-time, 40 hours per week. Flexible hybrid work arrangement with approximately 3 days on-site in the San Francisco office.

📌 Application Steps

To apply for this Senior Machine Learning Platform Engineer position at Strava:

1. Customize Your Resume: Highlight your relevant experience in machine learning platform engineering, MLOps, and production ML model deployment. Include any projects or achievements that showcase your ability to work with large datasets, extract insights, and inform product decisions.
2. Tailor Your Cover Letter: Explain why you are interested in this role and how your skills and experience make you a strong fit for the position. Be sure to mention any relevant projects or achievements that demonstrate your qualifications.
3. Prepare for Technical Phone Screen: Brush up on your machine learning platform engineering, MLOps, and production ML model deployment skills. Familiarize yourself with Strava's products, services, and technology stack.
4. Research Strava: Learn about Strava's mission, values, and culture. Understand the company's products, services, and technology stack. Prepare thoughtful questions to ask during the interview process.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and machine learning industry-standard assumptions. All details should be verified directly with Strava before making application decisions.