Principal Machine Learning Engineer, Headspace Health

• Master’s of Science degree or higher in Computer Science, Statistics, Mathematics or a related field OR equivalent experience
• 8+ years of ML engineering experience in an academic or professional setting, programming in Python
• 8+ 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
• 5+ 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

Desirable

• Machine Learning at Headspace is a dynamic and innovative group whose mission is to improve the experiences of our members and clinicians through the mindful application of Machine Learning
• These applications include building conversational AI systems, healthcare assistance tools, and recommendation and personalization systems
• In this team, you’ll be tasked with owning and delivering cutting edge language-based ML applications that will power the core features of Headspace
• You’ll have the opportunity to lead the vision, alignment, development, deployment, and adoption of these solutions, helping to bring Headspace to the forefront of AI and to realize its mission to improve health and happiness of the world
• Technical Leadership: Lead the development of complex, scalable AI models and applications from inception to production. Drive impactful ML technology initiatives that will shape the delivery of and access to mental healthcare. Serve as a go-to expert and mentor, exemplifying excellence in AI/ML engineering and inspiring others to pursue technical career growth
• Shape ML Platform Architecture: Drive the design, development, and evolution of our internal ML platform, taking it from high-level vision to robust implementation
• Collaborative Problem-Solving: Partner with cross-functional teams to align technical decisions with organizational goals, ensuring cohesive and impactful solutions