Machine Learning Engineer

We are hiring on behalf of our client, a global innovator in fitness and wellness technology. Their mission is to empower people to live fit, strong, long, and happy lives by delivering integrated experiences to millions of members anytime, anywhere.

• 3+ years of experience in machine learning, ideally in recommender systems, NLP, computer vision, or another applied ML domain.
• Degree in Computer Science, Machine Learning, Statistics, Mathematics, Operational Research, or another highly quantitative field.
• Hands-on experience building, training, evaluating, and improving machine learning models.
• Strong software engineering fundamentals, including data structures, algorithms, clean code, and reproducibility.
• Professional experience coding in Python; experience with Java, Kotlin, Go, C, or C++ is welcome.
• Experience working with relational or non-relational databases such as Postgres, MySQL, Cassandra, or DynamoDB.
• Familiarity with production ML systems, including deployment, monitoring, scalability, latency, and model-serving considerations.
• Strong communication skills with the ability to explain technical concepts clearly to business, technical, and non-technical audiences.

• Build, train, evaluate, and improve ML models powering personalized workout and content recommendations.
• Develop and maintain AI/ML pipelines using Python, Spark, Airflow, AWS, and S3.
• Research and apply modern machine learning techniques for recommender systems, content discovery, and engagement optimization.
• Productionize, deploy, monitor, and optimize ML models and recommendation services.
• Support the evolution of recommendation systems toward unified rankers, candidate generation, and real-time model serving.
• Run A/B tests and analyze experiment results with Product Analysts to measure product and user impact.
• Collaborate with ML Engineers, Software Engineers, Platform Engineers, Product Managers, and Product Analysts to test and launch new personalization ideas.
• Work with platform and data teams to use internal infrastructure, data sources, and tools required to scale ML solutions.