Director/ Senior Director, Applied AI
Lila SciencesCambridge, MA USA; San Francisco, CA USAPosted 26 March 2026
Job Description
Your Impact at Lila
Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method. We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before.
Your Impact at Lila
As the Director/Sr. Director of Applied AI you will be leading the build of our foundational Applied AI team that will fine-tune and adapt our models for real-world product use, make it safely accessible for diverse customer data, and deliver reliable interfaces (chat experiences and high-performance inference endpoints such as gRPC/HTTP/PCP servers). You will be reporting directly to the CTO and will own the roadmap from data to deployment, partner closely with Research, Product, Software, App, Safety, and Security teams, and ship models that are usable, measurable, and trustworthy.
What You'll Be Building
Build and lead a high-performing Applied AI team to deliver best-in-class scientific reasoning models for consumer applications
Own an end-to-end model customization pipeline to create valuable models from Lila’s base models for high-value use cases including inference, chat, and Lila’s API
Create an integrated evaluation framework for continuous model development and improvement
Partner with cross-functional teams to define requirements, deliver customer POCs, and transition successful pilots into scalable products
Set technical standards, documentation, and incident response for AI systems in production
Recruit, mentor, and grow a diverse team
What You’ll Need to Succeed
8+ years in applied ML/ML systems with 3+ years leading teams; track record shipping LLM systems to production
Deep experience fine-tuning and evaluating large models for customer interaction
Production inference expertise: serving frameworks (Triton, vLLM, TGI, Ray, Kubernetes), GPU orchestration, caching, and observability
Data privacy and security best practices for multi-tenant systems
Bonus Points For
Experience instruction fine tuning and hillclimbing therein.
Experience building chat UX, guardrails, tool/function calling, and long-context strategies
Knowledge of quantization/distillation, routing/mixture-of-experts, and cost optimization at scale
Experience working in compliance-heavy environments (HIPAA, PCI, FedRAMP) and on-prem/VPC deployments
About Lila
Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method. We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at
www.lila.ai
If this sounds like an environment you’d love to work in, even if you only have some of the experience listed below, we encourage you to apply.
We’re All In
Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy .
A Note to Agencies
Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisitio ... (truncated, view full listing at source)
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