Senior Machine Learning Engineer

🕒 April 12

Apply Now

Find Similar Remote Jobs

Receive Emails For Remote Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Strava

WebsiteLinkedInAll Job Openings

201 - 500 employees

👥 B2C

⚽ Sports

☁️ SaaS

💰 Venture Round - Strava on 2025-05

B2C • Sports • SaaS

Strava is a consumer-focused social fitness platform and mobile app that tracks and analyzes athletic activities (running, cycling, hiking and many other sports) using GPS and connected devices. It combines activity recording, detailed performance metrics, route planning, community features (clubs, challenges, feeds) and optional subscription services to help athletes train, connect and compete.

📋 Description

• 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

🎯 Requirements

• Have worked on numerous machine learning problems and broken them down into incremental tasks. • 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 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.

🏖️ Benefits

• For information on benefits, please click here.