Staff Machine Learning Engineer

Headspace is a company dedicated to providing mental health support through innovative technology. The Staff Machine Learning Engineer will lead the development of personalization systems, including content recommendation and conversational AI, to enhance the user experience for members and clinicians.

Responsibilities:

• Lead the development of recommender systems for Headspace meditation & mindfulness content as well as other backend services that enable a personalized member experience including search, a user knowledge graph, and conversational AI memory
• Contribute to the design, development, and evolution of our AI systems, taking it from high-level vision to robust implementation, enabling production-ready AI capabilities
• Partner with our team of software engineers, ML engineers, and MLOps engineers, and our Product & Clinical leads to build high quality features that improve members’ lives

Requirements:

• 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
• 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
• 3+ years of experience with modern NLP tools and machine learning libraries (scikit-learn, PyTorch, TensorFlow, spaCy)
• Experience with unit, integration, and end-to-end testing, version control
• Strong problem solving and communication skills and ability to influence across internal organizations
• Mentorship of junior engineers and contribution to DEIB initiatives
• Master's degree in relevant field or equivalent experience
• Professional experience with clinical and/or healthcare applications of machine learning
• Familiarity with current ML literature
• Experience with implementation of robust and highly scalable services
• Experience with AWS, including SageMaker, Lambda, S3, DynamoDB, IAM