AI Lab Research Engineer

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. Learn more about this mission at  www.lila.ai

At Lila, we are uniquely cross-functional and collaborative. We are actively reimagining the way teams work together and communicate. Therefore, we seek individuals with an inclusive mindset and a diversity of thought. Our teams thrive in unstructured and creative environments. All voices are heard because we know that experience comes in many forms, skills are transferable, and passion goes a long way.

If this sounds like an environment you’d love to work in, even if you only have some of the experience listed below, please apply.

What You'll Be Building

• Agentic AI for science: Systems that perform sequential decision‑making and multi‑step reasoning to solve domain‑specific problems.
• Workflow/code generation: From natural language intent to typed, executable steps for lab instruments.
• Evaluation & reliability: Benchmarks, test suites, and telemetry to measure capability and quantify progress toward scientific goals.

What You’ll Need to Succeed

• PhD or Masters in a quantitative discipline (e.g., Computer Science, Physics, Mathematics, Engineering) with a strong background in machine learning and one domain of science (e.g. biology or materials science).
• Strong grasp of LLMs and agent architectures (planning, tool use, structured function calling, code generation) and how to adapt them to domains.
• Proficiency in modern ML frameworks (e.g., PyTorch, TensorFlow, JAX) and experience implementing scalable solutions for complex tasks.
• Comfort collaborating across disciplines and interfacing with simulations and real lab systems.

Bonus Points For 

• Building long‑horizon agents or RL for control/decision‑making; experience with model‑based or offline RL.
• Designing domain‑specific benchmarks and evaluation harnesses for complex scientific tasks.