Lila Sciences

Senior / Staff Machine Learning Engineer, Applied AI

Cambridge, MA USA; San Francisco, CA USA · Staff+ · Full-time

Sponsorship not specified$180k-$336kDetected 20 days ago

PythonMachine LearningPyTorchLLMsRAGAgentic AIA/B TestingRecruitingResearchCommunication

About the role

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.

Build evaluation loops that measure model quality, reliability, and customer fit.
Design experiments to improve model performance across applied customer use cases.
Partner with AI researchers to translate model improvements into usable capabilities.
Build reusable tooling for model adaptation, evaluation, and deployment workflows.

Experience with distributed ML training frameworks (Megatron-LM, TorchTitan, DeepSpeed, Ray)

We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.
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 - $336,000 USD

Strong experience building, training, adapting, or evaluating machine learning models.

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.
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.

al employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status

This listing is sourced directly from Lila Sciences's careers page and normalized into a canonical job model.