Machine Learning Engineer, Strava
$160-175k
3+ days a week in office
Social network for tracking run and cycle routes
Open for applications
Social network for tracking run and cycle routes
501-1000 employees
Open for applications
$160-175k
3+ days a week in office
501-1000 employees
Company mission
To create the most committed, close, and engaged athlete community on the internet.
Role
Who you are
- Have worked on complex, ambiguous machine learning problems and broken them down into manageable tasks with both strategies and tactical execution
- Demonstrated technical leadership in leading projects and the ability to mentor and grow early career team members
- Have demonstrated strong interpersonal and communication skills, and collaborative approach to drive business impact across teams
- Have experience building, shipping, and supporting ML models in production at scale
- Have experience with exploratory data analysis and model prototyping, using languages such as Python or R and tools like Scikit learn, Pandas, Numpy, Pytorch, Tensorflow, Sagemaker
- Have built and worked on data pipelines using large scale data technologies (like Spark, Hadoop, EMR, SQL, Snowflake)
- Are experienced and interested in production ML model operational excellence and best practices, like automated model retraining, performance monitoring, feature logging, A/B testing
- Have built backend production services on cloud environments like AWS, using languages like (but not limited to) Python, Ruby, Java, Scala, Go
What the job involves
- We are looking for a Machine Learning Engineer to join the growing AI and Machine Learning team at Strava
- This team is responsible for sophisticated machine learning models and systems which provide value to Strava athletes including personalization, recommendations, search, and trust and safety
- The team also maintains the ML platform and infrastructure that enables our team to iterate on models quickly and deploy them reliably at scale
- This is an important role in the ML team and across Product teams designing, roadmapping, and implementing innovative machine learning algorithms
- We value full stack ML engineers who are able to work on all parts of an ML pipeline from model building, evaluation, optimizing performance, and ensuring the scalability and reliability of these production models
- We also seek those who can improve the systems and tools behind the ML pipeline to further empower the team
- 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
- Own End to End AI Systems: Lead key projects powered by ML on the Strava platform end-to-end, from initial model prototyping to shipping production code to scaling and optimizing inference and deployment
- 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 in multiple surfaces and build out our technical ML capabilities
- Innovate in AI for Fitness: Design and develop novel models and methodologies to take on novel problems in that improve athlete experience, including recommendation systems, activity prediction, and personalized insights
- Build from a rich dataset: Explore and use Strava’s extensive unique fitness and geo datasets from millions of users to extract actionable insights, inform product decisions, and optimize existing features
- Driving innovation with Product in mind: Stay up-to-date with the latest research in machine learning, AI, and related fields. Experiment, advocate and get buy-in for innovative techniques to improve existing products or explore new features that result in step function changes to how we build AI at Strava
- Leading as an Owner: Owning your work end-to-end and being accountable for the outcomes in the projects you lead, influencing the ML team, partner teams and landing impact for the business. Ensure the end to end system delivers as expected through collaboration with partners
- Analyzing the Data: Work closely with product managers, data scientists, and engineers to find opportunities for applying machine learning to drive business impact and enhance Strava’s features and measure impact
- Collaborating in and across teams: Build relationships, advocate and communicate with crossfunctional partners and product vertical to identify opportunities and bring your technical vision to life
- Raising the ML standard: Mentor engineers to shape how we do ML at Strava. Drive best practices for model development, deployment, and maintenance and be a go-to source of knowledge of the field
- Being passionate about the work you are doing and contributing positively to Strava’s inclusive and collaborative team culture and values
- We follow a flexible hybrid model that generally translates to around half your time on-site in our San Francisco Office—roughly three days per week
Our take
Strava has created an online network where runners, swimmers and cyclists can record fitness activities, and compare and compete with their community. It offers both a free and premium subscription service with added features. The company also partners with local councils and urban planners who purchase valuable journey data from Strava which can be used to map out cities and develop safer cycle paths.
The platform helps members discover and plan workouts, as well as offering a leaderboard which is updated using GPS as users compete for the top spot on particular fitness segments.
With over 125 million athletes and growing, Strava released several new product rollouts in 2024, including an AI-enabled leaderboard. A partnership with the wearable fitness tracker Oura will mean Strava software is available on wearables, taking Strava into fresh competition with the likes of Peloton and Apple.
Freddie
Company Specialist at Welcome to the Jungle
Insights
-6% employee growth in 12 months
Company
Funding (last 2 of 5 rounds)
Nov 2020
$110m
SERIES F
Oct 2014
$18m
LATE VC
Total funding: $151.4m
Company benefits
- Competitive base salary & stock options
- 100% company paid benefits for employees and families
- Flexible paid time off
- $2,000 annual professional development stipend
- Paid time off for volunteering
- 401(k) plan - with company matching
- $1000 annual gear stipend
- $500 annual gym or coaching reimbursement
- On-site fitness rooms with showers, lockers, and towel service
- Weekly team workouts – including remote employees
- Safe and secure bike storage on-site
- Weekly all hands meetings
- Employee resource groups
- Anti-racism nonprofit company matching program
- Free lunch on Wednesdays on-site
- Regular team happy hours
- Team offsites and company retreats
- Cell phone reimbursement
- Generous industry discounts on gear and activities
- Race entries to events we sponsor
- Snacks and stocked kitchens on-site
- Strava gear
Company HQ
Mission District, San Francisco, CA
Leadership
Mark Gainey
(Chairman)Having studied at Harvard, Mark started their career as a TA Associates Associate before co-founding Kana Communications and several Board Membership roles. They co-founded Strava in 2007, serving since as Chairman.
Michael Horvath
(CEO & Board Member)Michael co-founded Kana with Mark before working as an Economics Professor at Stanford and Dartmouth. They worked for 4 years as CFO of GlycoFi before co-founding Strava, serving initially as CEO and since 2017 as a Board Member.
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