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Machine Learning EngineerAlumni Ventures • San Francisco, California, United States
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Machine Learning Engineer

Alumni Ventures
  • US
    San Francisco, California, United States
  • US
    San Francisco, California, United States
Jetzt Bewerben

Über

Location Strava SF
Employment Type Full time
Location Type Hybrid
Department Department Technology Engineering
Compensation
$160K – $180K • Offers Equity
This range reflects base compensation only and does not include equity or benefits. Your recruiter can share more details about the full compensation package during the hiring process.
At Strava, we know our employees are the most important ingredient to our success, and our compensation and total rewards programs reflect that. We take a market-based approach to pay, and pay may vary depending on the department and your location. Salary ranges are categorized into one of three zones based on a cost of labor index for that geographic area. We will determine the candidate’s starting pay based on job-related skills, experience, qualifications, work location, and market conditions. We may modify these ranges in the future.
For more information, please contact your talent partner.
About Strava Strava is the app for active people. With over 180 million athletes in more than 185 countries, it’s more than tracking workouts—it’s where people make progress together, from new habits to new personal bests. No matter your sport or how you track it, Strava’s got you covered. Find your crew, crush your goals, and make every effort count. Start your journey with Strava today.
Our mission is simple: to motivate people to live their best active lives. We believe in the power of movement to connect and drive people forward.
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.
We follow a flexible hybrid model that translates to more than half your time on-site in our San Francisco Office — three days per week.
What You’ll Do:
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
Craft End to End AI Systems : Contribute to 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 : Bring your voice and creativity to 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 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
You Will Be Successful Here By:
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.
Be Accountable : Owning your work end-to-end and being accountable for the outcomes in the projects you contribute to. 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 and communicate with cross-functional partners and product verticals to identify opportunities and bring the team’s technical vision to life.
Raising the ML standard : Use best practices for model development, deployment, and maintenance. Keep a high bar for quality in everything you do.
Being passionate about the work you are doing and contributing positively to Strava’s inclusive and collaborative team culture and values
What You’ll Bring to the Team:
Have worked on impactful machine learning problems delivering incremental progress towards long-term goals.
Have demonstrated solid interpersonal and communication skills, and collaborate 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, and Sagemaker.
Have built and worked on data pipelines using large scale data technologies (like Spark, Hadoop, EMR, SQL, and Snowflake).
Are experienced and interested in production ML model operational excellence and best practices, like automated model retraining, performance monitoring, feature logging, and A/B testing.
Have built backend production services on cloud environments like AWS, using languages like (but not limited to) Python, Ruby, Java, Scala, and Go.
For information on benefits, please click here.
Why Join Us? Movement brings us together. At Strava, we’re building the world’s largest community of active people, helping them stay motivated and achieve their goals.
Our global team is passionate about making movement fun, meaningful, and accessible to everyone. Whether you’re shaping the technology, growing our community, or driving innovation, your work at Strava makes an impact.
When you join Strava, you’re not just joining a company—you’re joining a movement. If you’re ready to bring your energy, ideas, and drive, let’s build something incredible together.
Strava builds software that makes the best part of our athletes’ days even better. Just as we’re deeply committed to unlocking their potential, we’re dedicated to providing a world‑class, inclusive workplace where our employees can grow and thrive, too. We’re backed by Sequoia Capital, TCV, Madrone Partners and Jackson Square Ventures, and we’re expanding in order to exceed the needs of our growing community of global athletes. Our culture reflects our community. We are continuously striving to hire and engage teammates from all backgrounds, experiences and perspectives because we know we are a stronger team together.
Strava is an equal opportunity employer. In keeping with the values of Strava, we make all employment decisions including hiring, evaluation, termination, promotional and training opportunities, without regard to race, religion, color, sex, age, national origin, ancestry, sexual orientation, physical handicap, mental disability, medical condition, disability, gender or identity or expression, pregnancy or pregnancy-related condition, marital status, height and/or weight.
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
California Consumer Protection Act Applicant Notice
Compensation Range: $160K - $180K
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  • San Francisco, California, United States

Sprachkenntnisse

  • English
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