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Senior / Engineer II, AI Lab Research Engineer

External
lilasciences logoLilasciences · Cambridge, UK
$148K–$240K/yrFull-timeOn-site1mo ago
LLMsMachine LearningMovePyTorchTensorFlow
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Benefits

We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.Expected Base Salary Range$148,000 - $240,000 USDAbout LILALila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.We're All InLila 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 AgenciesLila 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 proHealth insuranceDental insuranceVision insuranceFlexible scheduleEquity / stock optionsPerformance bonusParental leave

Additional Information

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. Digital‑twin development, calibration, and sim‑to‑real transfer. Publications or open‑source contributions in AI for science (especially publications in top-tier conferences like NeurIPS, ICML, AAAI, ICLR). Location San Francisco, CA or Cambridge, MA (Hybrid and On-Site available depending on team needs).


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