Lead AI Engineer , Woebot Labs

• 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

Desirable

• 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
• 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
• 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
• 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
• 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
• 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