Principal Machine Learning Engineer, Applied AI

Your Impact at LILA We are growing our Applied AI org and seeking a Principal Machine Learning Engineer, Applied AI with deep expertise in model post-training, evaluation, and production-oriented ML systems. You’ll shape how Lila’s AI models are adapted to customer-specific scientific needs, with a focus on turning frontier model capabilities into reliable workflows that can be evaluated, iterated and used in real customer contexts. Applied AI sits at the intersection of AI Research, model engineering, and product deployment. The team partners closely with AI Researchers and Software teams to adapt Lila models to customer workflows, improve model quality through post training and driving how harness engineering shapes model behavior inside the product. This is a high-impact senior IC role for someone who brings deep, hands-on expertise in model post-training, evaluation, and production ML systems, built over many years of solving this exact class of problem. What You'll Be Building - Apply deep, hands-on expertise to close the last-mile gap between Lila's model capabilities and customer-specific scientific workflows. - Lead post-training efforts using approaches such as SFT and RL (DPO, PPO/GRPO) to align model behavior with customer-specific requirements and feedback. - Build and run evaluation loops that measure model quality, reliability, and customer fit, using patterns you've refined across many prior projects. - Turn customer learnings, data signals, and evaluation results into concrete model improvement cycles. - Partner with AI researchers to translate model advances into reliable, usable capabilities. - Work with Software to integrate model behavior into end-to-end product workflows. - Debug complex model failures using traces, evaluations, customer context, and scientific feedback. - Mentor engineers and share reusable patterns for model adaptation, evaluation, and deployment, drawing on lessons learned across a long track record of shipping ML systems. What You'll Need to Succeed - Minimum 2-3 years of hands-on post-training experience (SFT, RL methods such as DPO/PPO/GRPO) and evaluation system design, gained from having solved these problems many times before. - Strong software engineering skills in Python and modern ML frameworks like PyTorch. - A demonstrated ability to debug ambiguous, high-stakes model behavior quickly, using data, traces, logs, and qualitative feedback - the kind of judgment that only comes from having seen many failure modes before. - Experience leading technical work across research and engineering teams. - Deep familiarity with large language models, multi-modal models, or agentic AI systems. - Clear communication skills for translating customer needs into technical approaches, and for explaining complex model behavior to both technical and non-technical audiences. Bonus Points For - Experience adapting models for customer-facing or production workflows, ideally in scientific, technical, or data-intensive domains. - Experience with RL post-training, such as RLHF, GRPO, or tool-augmented RL. - Experience building evaluation harnesses, model monitoring, or quality dashboards. - Experience training MoE architectures. - A track record of mentoring engineers and being sought out as a go-to technical expert, rather than a people manager or strategy owner. Compensation We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact. U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program. 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 $252,000—$336,000 USD About LILA Lila 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. LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai. 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 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 Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.