Engineering Manager, AI

Headspace

9h•$179,400 - $250,000•Hybrid

About The Position

The AI & Machine Learning group 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 AI. Our group builds conversational AI systems including Ebb, as well as search, recommendation & personalization systems that power the Headspace experience. The Engineering Manager will lead the delivery of cutting edge language-based AI applications and backend services behind the core features of Headspace. You’ll have the opportunity to lead the vision, alignment, development, deployment, and evangelization of these solutions, helping to realize Headspace’s mission to improve the health and happiness of the world.

Requirements

Bachelor’s of Science degree in Computer Science, Statistics, Mathematics or a related field OR equivalent experience
6+ years of engineering experience building python applications
2+ years of experience managing and leading high performing teams
1+ years of experience as an engineer building systems with a generative AI component.
Experience deploying applications with AWS
Strong problem solving and communication skills and ability to influence across internal organizations
Experience as a people manager with mentorship of junior engineers and contribution to DEIB initiative

Nice To Haves

Experience with any of the following fundamental technologies: vector search, embedding models, recommender systems, LLM orchestration, RAG systems, chatbots.
Professional experience with clinical and/or healthcare applications and HIPAA compliance.
Experience with Typescript
Experience with AWS SageMaker

Responsibilities

Technical Leadership: Lead the development and delivery of complex, scalable software systems that support AI models and capabilities. Drive impactful technology initiatives that will leverage AI to shape and democratize the delivery of and access to mental healthcare.
Shape AI Architecture: Lead the design, development, and evolution of our AI systems, taking it from high-level vision to robust implementation, enabling production-ready AI capabilities.
Team Management: Manage a small team of senior software engineers in a player/coach model that allows you to be hands-on developing more complex features, as well as with architectural decisions and code reviews. Prioritize kindness and empathy in your management style. Build your team by hiring for additional engineering roles as they open.
Collaborative Problem-Solving: Partner with AI/ML leads, MLOps leads, and our Product & Clinical leads to make joint technical decisions that result in high quality features that improve members’ lives.
Champion Code Quality: Set the bar for quality of our applications, exemplifying excellence in engineering for AI/ML and inspiring others to deliver high quality software systems. Advocate for and contribute to high-quality engineering standards through rigorous code reviews and constructive, actionable feedback.