Senior / Staff Machine Learning Engineer, Applied AI

Lila Sciences•Cambridge, MA

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.