The ad says“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”
The ad says“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”
The ad says“Have built backend production services on cloud environments like AWS, using languages like (but not limited to) Python, Ruby, Java, Scala, and Go”
The ad says“Have built and worked on data pipelines using large scale data technologies (like Spark, Hadoop, EMR, SQL, and Snowflake)”
The ad says“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”
The ad says“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”
The ad says“Have built backend production services on cloud environments like AWS, using languages like (but not limited to) Python, Ruby, Java, Scala, and Go”
The ad says“Have built and worked on data pipelines using large scale data technologies (like Spark, Hadoop, EMR, SQL, and Snowflake)”
The ad says“Have built backend production services on cloud environments like AWS, using languages like (but not limited to) Python, Ruby, Java, Scala, and Go”
The ad says“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”
The ad says“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”
The ad says“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”
The ad says“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”
The ad says“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”
The ad says“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”
The ad says“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”
The ad says“Have built backend production services on cloud environments like AWS, using languages like (but not limited to) Python, Ruby, Java, Scala, and Go”
The ad says“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”
The ad says“Have built backend production services on cloud environments like AWS, using languages like (but not limited to) Python, Ruby, Java, Scala, and Go”
The ad says“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”
The ad says“Have built and worked on data pipelines using large scale data technologies (like Spark, Hadoop, EMR, SQL, and Snowflake)”
The ad says“Have built and worked on data pipelines using large scale data technologies (like Spark, Hadoop, EMR, SQL, and Snowflake)”
The ad says“Have built and worked on data pipelines using large scale data technologies (like Spark, Hadoop, EMR, SQL, and Snowflake)”
The ad says“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”
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: Drive 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 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 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 drive 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 cross-functional partners and product verticals to identify opportunities and bring your technical vision to life
Raising the ML standard: Help work towards best practices for model development, deployment, and maintenance
Being passionate about the work you are doing and contributing positively to Strava’s inclusive and collaborative team culture and values
Benefits
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
Stock options
Preferred experience
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 worked on numerous machine learning problems and broken them down into incremental tasks
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
Have demonstrated solid 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 built and worked on data pipelines using large scale data technologies (like Spark, Hadoop, EMR, SQL, and Snowflake)
