Principal Machine Learning Engineer

Headspace is a company dedicated to providing lifelong mental health support through innovative technology. They are seeking a Principal Machine Learning Engineer to lead the development of AI applications that enhance mental healthcare delivery and improve user experiences. The role involves technical leadership, shaping the ML platform architecture, and collaborating with cross-functional teams to align technical solutions with organizational goals.

Responsibilities:

• 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
• Drive the design, development, and evolution of our internal ML platform, taking it from high-level vision to robust implementation
• Partner with cross-functional teams to align technical decisions with organizational goals, ensuring cohesive and impactful solutions

Requirements:

• 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
• PhD in relevant field or equivalent experience
• Professional experience with clinical and/or healthcare applications of machine learning
• Familiarity with current ML literature including optimization methods and agent-based models
• Experience with implementation of robust and highly scalable services
• Experience with AWS, including SageMaker, Lambda, S3, DynamoDB, IAM