Job description

Your Impact at LILA We are growing our Applied AI org and seeking talented Senior/Staff Machine Learning Engineers with expertise in LLM training, evaluation, and production-oriented ML systems. You’ll work on improving Lila’s AI models for 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. This is a rare chance to join an early team with the autonomy, flexibility, and compute to tackle frontier science problems. 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 experimentation, and ensure model behavior works well end to end inside the application. This role is ideal for someone who can bridge research and engineering: training or adapting models, building evaluation loops, debugging model behavior, and collaborating across AI and Software to move promising capabilities into production-quality systems. What You'll Be Building Close the last-mile gap between Lila AI model capabilities and customer-specific scientific workflows. Build evaluation loops that measure model quality, reliability, and customer fit. Design experiments to improve model performance across applied customer use cases. Feed customer learnings, data signals, and evaluation results back into the Lila AI model improvement cycles. Partner with AI researchers to translate model improvements into usable capabilities. Work with Software to integrate model behavior into end-to-end product workflows. Debug model failures using traces, evaluations, customer context, and scientific feedback. Build reusable tooling for model adaptation, evaluation, and deployment workflows. What You'll Need to Succeed Strong experience building, training, adapting, or evaluating machine learning models. Strong software engineering skills in Python and modern ML frameworks such as PyTorch, JAX, or TensorFlow. Experience with distributed ML training frameworks (Megatron-LM, TorchTitan, DeepSpeed, Ray) Experience designing experiments, evaluation metrics, or test sets for model performance. Ability to debug model behavior using data, traces, logs, and qualitative feedback. Experience working across research and engineering teams to move ML capabilities into usable systems. Familiarity with large language models, multi-modal models, or agentic AI systems. Clear communication skills for translating customer needs into technical model improvements. Bonus Points For Experience adapting models for customer-facing or production workflows. Experience with scientific, technical, or data-intensive customer use cases. Experience building evaluation harnesses, model monitoring, or quality dashboards. Familiarity with retrieval-augmented generation, tool use, or agentic workflows. Experience with RL post-training, such as RLHF, GRPO, or tool-augmented RL. Experience training MoE architectures. Experience working with product or customer-facing teams to translate needs into ML improvements. 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 $180,000 — $298,000 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.

Requirements

Must-haves

Your Impact at LILA
What You'll Be Building
Close the last-mile gap between Lila AI model capabilities and customer-specific scientific workflows.
Build evaluation loops that measure model quality, reliability, and customer fit.
Design experiments to improve model performance across applied customer use cases.
Feed customer learnings, data signals, and evaluation results back into the Lila AI model improvement cycles.
Partner with AI researchers to translate model improvements into usable capabilities.
Work with Software to integrate model behavior into end-to-end product workflows.
Debug model failures using traces, evaluations, customer context, and scientific feedback.
Build reusable tooling for model adaptation, evaluation, and deployment workflows.
What You'll Need to Succeed
Strong experience building, training, adapting, or evaluating machine learning models.
Strong software engineering skills in Python and modern ML frameworks such as PyTorch, JAX, or TensorFlow.
Experience with distributed ML training frameworks (Megatron-LM, TorchTitan, DeepSpeed, Ray)
Experience designing experiments, evaluation metrics, or test sets for model performance.
Ability to debug model behavior using data, traces, logs, and qualitative feedback.
Experience working across research and engineering teams to move ML capabilities into usable systems.
Familiarity with large language models, multi-modal models, or agentic AI systems.
Clear communication skills for translating customer needs into technical model improvements.
Experience adapting models for customer-facing or production workflows.
Experience with scientific, technical, or data-intensive customer use cases.
Experience building evaluation harnesses, model monitoring, or quality dashboards.
Familiarity with retrieval-augmented generation, tool use, or agentic workflows.

Nice-to-haves

Bonus Points For

Application prep

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Keywords to mirror

aimachine learningllmmlpython

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

Parental Leave