Staff Machine Learning Engineer

Join Headspace, a leading mental health and wellness company, as a Staff Machine Learning Engineer. In this role, you will lead the development of personalized AI systems that enhance the experiences of our members and clinicians. You will have the opportunity to shape the future of mental healthcare through impactful ML technology initiatives.

• Lead the development of recommender systems for Headspace meditation & mindfulness content, as well as other backend services that enable a personalized member experience.
• Contribute to the design, development, and evolution of AI systems, taking it from high-level vision to robust implementation, enabling production-ready AI capabilities.
• Partner with a team of software engineers, ML engineers, and MLOps engineers, and Product & Clinical leads to build high-quality features that improve members’ lives.
• Strong problem solving and communication skills and ability to influence across internal organizations
• 3+ years of experience with modern NLP tools and machine learning libraries (scikit-learn, PyTorch, TensorFlow, spaCy)
• 5+ years of experience with any of the following fundamental technologies: vector search, embedding models, recommender systems, supervised, unsupervised machine learning, deep learning, reinforcement learning, LLM orchestration, RAG systems
• Mentorship of junior engineers and contribution to DEIB initiatives
• Experience with unit, integration, and end-to-end testing, version control
• Bachelor of Science degree or higher in Computer Science, Statistics, Mathematics or a related field OR equivalent experience
• 5+ years of ML engineering experience in an academic or professional setting, programming in Python
• Professional experience with clinical and/or healthcare applications of machine learning
• Master’s degree in relevant field or equivalent experience
• Experience with AWS, including SageMaker, Lambda, S3, DynamoDB, IAM
• Experience with implementation of robust and highly scalable services
• Familiarity with current ML literature