About Woebot Health
We’re a mission-driven 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 human-centered technology. Using the latest advances in language models, real-time interactions, and conversational design, we’re creating a next-generation digital companion that helps people feel seen, supported, and empowered - wherever they are, whenever they need it. With backing from top-tier investors and a bold vision for the future, we’re moving fast, learning constantly, and designing for real-world 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 top-tier investors and powered by a hands-on 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 fine-tuning 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 long-form, multi-turn interactions - prioritizing emotional coherence and user outcomes over token accuracy
Stay on top of rapid developments in LLMs, fine-tuning frameworks, and inference efficiency, translating that knowledge into action
Champion best practices for scaling training workflows, experimenting safely, and continuously learning from real-world 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 real-world 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 multi-turn interactions using frameworks like DSPy
Build prompt libraries and A/B test variants to optimize for safety, clarity, and on-brand responsiveness
Leverage prompt engineering as a short-term strategy where fine-tuning is not yet appropriate, with a clear view on trade-off
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 Fine-Tuning & Optimization
Own parameter-efficient fine-tuning pipelines (e.g., LoRA, QLoRA) to adapt foundational models to brand-specific voice, tone, and emotional range
Curate high-quality datasets and design eval metrics tailored to coherence, empathy, and state consistency across sessions
Explore model compression, quantization, and inference optimization for low-latency 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 human-in-the-loop annotation systems where automation falls short
Ship prototypes and production features rapidly, with a build-learn-refine approach
What We’re Looking For
Deep Technical Fluency
5+ years in AI/ML engineering, with at least 2 years of hands-on fine-tuning large language models
Demonstrated expertise in applied AI within early-stage 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 trade-offs between fine-tuning, tool invocation, prompt orchestration, and hybrid approaches
Proficiency with model training workflows, scalable data pipelines, and LLM evaluation techniques
Practical experience with low-latency inference environments and model optimization strategies (quantization, compression, routing logic)
Comfortable in Python and modern ML tooling; experienced deploying models to production environments
Product-Oriented 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 non-technical partners alike
Demonstrated curiosity for emerging methods and a track record of staying current on deep learning advancements
Collaborative, Bold & Humble
Team-first 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 small-team 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 AI-first 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: One-time home office setup stipend
Security: Company-sponsored 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.
