Lead AI Engineer

About Woebot Health

We’re a missiondriven startup reinventing the way people find peace and inspiration through dazzling digital experiences. Our team blends engineering, design, and product minds with a shared passion for building humancentered technology. Using the latest advances in language models, realtime interactions, and conversational design, we’re creating a nextgeneration digital companion that helps people feel seen, supported, and empowered wherever they are, whenever they need it. With backing from toptier investors and a bold vision for the future, we’re moving fast, learning constantly, and designing for realworld impact at scale.

We believe the future of generative AI lies not in novelty, but in precision, nuance, and emotional resonance. If you’re driven by solving hard problems in applied AI and want to shape systems that genuinely serve people, we’d love to meet you.

Why Join Us?

Our company was founded by Alison Darcy, a clinical research psychologist and digital health entrepreneur. Among our earliest and most strategic backers is AI pioneer Andrew Ng, founder of AI Fund and deeplearning.ai. Backed by toptier investors and powered by a handson team of passionate creators, we’re turning bold ideas into transformative digital care. This role will report to and collaborate closely with our founder, shaping the architecture and capabilities of our AI companion from the ground up. Joining our small but mighty core team means making an outsized and immediate impact on people’s wellness.

The Role

Our LeadAI Engineer will help architect and evolve the “cognitive engine” of our platform. This role sits at the intersection of model tuning, inference optimization, and intelligent orchestration, focused on building adaptive systems that can shift modes fluidly depending on user needs.

You’ll partner closely with our psychology, product, and infrastructure teams to:

• Lead finetuning of foundational models using efficient training techniques and custom datasets

• Design and implement model orchestration logic that determines when to retrieve, route, generate, or escalate across different conversational contexts

• Build and iterate on eval frameworks for longform, multiturn interactions prioritizing emotional coherence and user outcomes over token accuracy

• Stay on top of rapid developments in LLMs, finetuning frameworks, and inference efficiency, translating that knowledge into action

• Champion best practices for scaling training workflows, experimenting safely, and continuously learning from realworld feedback

• While the company currently operates in a remote environment, we aspire to build a hybrid presence in the Bay Area, which may require relocation down the line. While we may prioritize talent based in the Bay Area, we are open to hiring top talent anywhere in the US.

  What You’ll Do

  As a LeadAI Engineer, you will be responsible for architecting, optimizing, and evolving the “cognitive engine” of our AI companion. Your work will combine deep model training expertise with realworld experimentation, striking a balance between precision, nuance, and adaptability. You’ll collaborate across AI, product, and design to translate emotional and behavioral intent into reliable, scalable machine intelligence, and help define our technical roadmap in collaboration with the founder & engineering team.

  You will:

  • Prompt Engineering & Optimization

    • Design, iterate, and evaluate prompt strategies for complex multiturn interactions using frameworks like DSPy

    • Build prompt libraries and AB test variants to optimize for safety, clarity, and onbrand responsiveness

    • Leverage prompt engineering as a shortterm strategy where finetuning is not yet appropriate, with a clear view on tradeoff

    • • Agentic Reasoning & Orchestration

        • Evaluate and integrate modular orchestration strategies (e.g., LangGraph, LlamaIndex, Letta, PydanticAI), forming a perspective on their relevance and scalability

        • Design systems that can switch between reflection, coaching, or directive states based on context, using either routing logic or learned behavior

        • Collaborate with the product team to define how tools, memory, and reasoning modules interact without overcomplicating the user experience

        • • Model FineTuning & Optimization

            • Own parameterefficient finetuning pipelines (e.g., LoRA, QLoRA) to adapt foundational models to brandspecific voice, tone, and emotional range

            • Curate highquality datasets and design eval metrics tailored to coherence, empathy, and state consistency across sessions

            • Explore model compression, quantization, and inference optimization for lowlatency voice and mobile interactions

            • • Experimental Thinking & Evaluation

                • Design lightweight experiments to validate technical approaches and measure outcomes beyond accuracy (e.g., trust, emotional congruence)

                • Partner with domain experts to implement humanintheloop annotation systems where automation falls short

                • Ship prototypes and production features rapidly, with a buildlearnrefine approach

                • • What We’re Looking For

                    Deep Technical Fluency

                    • 5+ years in AIML engineering, with at least 2 years of handson finetuning large language models

                    • Demonstrated expertise in applied AI within earlystage startups or product teams you can speak to the lived experience of leading teams and projects through rapid growth and production.

                    • Strong understanding of the tradeoffs between finetuning, tool invocation, prompt orchestration, and hybrid approaches

                    • Proficiency with model training workflows, scalable data pipelines, and LLM evaluation techniques

                    • Practical experience with lowlatency inference environments and model optimization strategies (quantization, compression, routing logic)

                    • Comfortable in Python and modern ML tooling; experienced deploying models to production environments

                    • ProductOriented Thinker

                      • Ability to translate product or psychological intent into model architecture or training strategy

                      • Prior work in behavioral health, mental wellness or adjacent domains, demonstrating sensitivity to emotionally resonant interactions.

                      • Experimental mindset with a bias toward measurable learning and iterative improvement

                      • Strong communicator who can explain the “why” behind the “how” to technical and nontechnical partners alike

                      • Demonstrated curiosity for emerging methods and a track record of staying current on deep learning advancements

                      • Collaborative, Bold & Humble

                        • Teamfirst engineer who values listening as much as leading, who can mentor engineers in best practices while staying open to feedback

                        • Comfortable with challenging ideas while seeking the best solution, not credit

                        • Motivated by impact and aligned to our mission of building AI that helps people

                        • Adaptable to smallteam dynamics and comfortable operating as the technical leader in a flat team structure, collaborating closely with product & engineering teams.

                        • Bonus if You Have

                          • Experience at AIfirst companies

                          • Experience building products in the behavioral health or digital wellness space.

                          • Knowledge of conversational state management, memory systems, or emotional alignment in LLMs

                          • Exposure to orchestrated AI frameworks or modular agentic architectures

                          • What We Offer

                            • Compensation: $180,000 $225,000 base + equity

                            • Benefits: Medical, dental, and vision for you and your family

                            • Time Off: Flexible PTO and mental wellness support

                            • Learning Stipend: Annual budget for courses, conferences, or certifications

                            • Remote Support: Onetime home office setup stipend

                            • Security: Companysponsored life and disability insurance + 401(k)

                            • We are committed to fostering an inclusive and equitable workplace. Compensation decisions are based on a variety of factors, including location, skills, experience, and various market benchmarks.