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About the Role
We are growing our Applied AI org and seeking Machine Learning Engineers with expertise in model 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.
Responsibilities
- Close the last-mile gap between Lila AI model capabilities and customer-specific scientific workflows.
- Post-train models using approaches such as SFT and RL (DPO, PPO/GRPO) to align model behavior with customer-specific requirements and feedback.
- 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.
Requirements
- 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 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.
Skills
- Python
- PyTorch
- JAX
- TensorFlow
- Large language models
- Multi-modal models
- Agentic AI systems
- SFT
- RL (DPO, PPO/GRPO)
- Retrieval-augmented generation
- Tool use
- Agentic workflows
- RLHF
- GRPO
- Tool-augmented RL
- MoE architectures
Location
- U.S.
Work Type
- Full-time
Experience Level
- Early-stage
Salary/Compensations
- $116,000—$170,000 USD
Benefits
- Medical coverage
- Dental coverage
- Vision coverage
- Employer-paid life insurance
- Disability insurance
- Flexible time off
- Generous company wide holidays
- Paid parental leave
- Educational assistance program
- Commuter benefits
- Bike share memberships
- Company subsidized lunch program
About the Company
- 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.
- Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance.
Equal Opportunity
- 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.