Director, AI Architect

Headspace•San Francisco, CA

5d•Hybrid

About The Position

At Headspace, our mission is to transform mental healthcare from an episodic need to an everyday care practice by offering an AI-powered, clinically safe, and personalized Mental Health Companion. AI is central to how we fulfill that mission at scale. As our Director, AI Architect, you will own the technical vision and execution of Headspace's AI-first service transformation, designing the systems, patterns, and practices that embed intelligence across every layer of our product and platform. Reporting directly to the Chief Product & Engineering Officer, you will operate at the intersection of applied research, ML science, and platform engineering. This is a rare opportunity to shape how a category-defining mental health company thinks about, builds with, and deploys AI, responsibly, scalably, and in ways that genuinely improve member outcomes.

Requirements

10+ years of software engineering experience, with at least 4 years focused on the design and delivery of production AI/ML systems at scale.
Deep expertise in modern AI architectures, including LLMs, RAG systems, embedding pipelines, agentic frameworks, and real-time inference, with hands-on experience moving these from prototype to production.
Proven ability to define AI strategy at an organizational level: translating ambiguous business challenges into technical roadmaps, influencing executive stakeholders, and driving alignment across cross-functional teams.
Strong command of responsible AI principles: safety, fairness, explainability, data privacy, and the unique ethical considerations of AI in health and wellness contexts.
Extensive experience with cloud-native AI infrastructure (AWS, GCP, or Azure), containerized deployment (Kubernetes), and MLOps practices including model serving, monitoring, and evaluation pipelines.
Demonstrated ability to evaluate and integrate third-party AI providers, orchestration frameworks (e.g., LangChain, LlamaIndex, or similar), and vector/embedding database systems.
Exceptional communication skills: you can articulate complex AI trade-offs clearly to both technical engineers and non-technical executives, and write specs that bring entire organizations along with you.
Ownership mindset: you are comfortable navigating ambiguity, making consequential architectural decisions with incomplete information, and taking accountability for outcomes across teams.

Nice To Haves

BS/MS/PhD in Computer Science, Machine Learning, or a related field, or equivalent practical experience.
Experience in digital health, wellness, or a similarly regulated consumer domain, with familiarity with HIPAA, data minimization practices, and the heightened standard of care for AI in sensitive user contexts.
Background in fine-tuning, RLHF, or domain-adapted model training for specialized consumer applications.
Experience with conversational AI, dialogue systems, or AI-powered coaching/companionship products.
Familiarity with Server-Driven UI (SDUI) and how AI-driven personalization integrates with dynamic, schema-based rendering across web and mobile clients.
Track record of building AI evaluation frameworks — including automated evals, red-teaming, and human-in-the-loop review pipelines — to maintain quality at scale.
Experience driving AI governance initiatives, including model cards, audit trails, and cross-functional risk review processes.

Responsibilities

Define and lead Headspace's overarching AI architecture strategy, establishing the foundational patterns, platforms, and principles for an AI-first service transformation across our portfolio of streaming content, conversational AI, coaching, therapy and psychiatry services.
Architect end-to-end AI systems: including LLM-powered features, agentic workflows, retrieval-augmented generation (RAG), and real-time personalization, with a relentless focus on reliability, safety, and member impact.
Partner directly with the executive team, product leadership, and senior engineering stakeholders to align AI strategy with company goals, translating business opportunities into concrete technical roadmaps.
Author company-wide technical specs that establish AI design principles, evaluation frameworks, guardrails, and reusable platform components.
Drive responsible AI practices across the organization, including model evaluation, bias mitigation, explainability, data governance, and compliance with evolving regulatory standards relevant to health tech.
Lead the selection, evaluation, and integration of AI/ML infrastructure, including model providers, vector databases, orchestration frameworks, and MLOps tooling, balancing build vs. buy decisions with long-term strategic implications.
Collaborate with Data Science, ML Engineering, and Product teams to ensure AI systems are grounded in high-quality, privacy-preserving data pipelines and continuously improve through rigorous feedback loops.
Establish AI engineering standards and best practices across squads, from prompt engineering and context management to model versioning, observability, and production monitoring.
Mentor and elevate engineers, ML practitioners, and technical leads across the organization, helping teams build confidence and competency in applied AI development.
Serve as Headspace's internal and external thought leader on AI, representing the company's technical vision in recruiting, partnerships, and the broader industry.
Identify and evaluate emerging AI capabilities (reasoning models, multimodal systems, fine-tuning approaches) for near-term applicability to Headspace's roadmap.