Applied AI ML Engineer: Bridge Research to Production

Lila Sciences; Cambridge, San Francisco (MA, CA); On-site; USD 116,000 - 170,000; Full time

Benefits offered by this job

Medical, dental, and vision coverage

Life and disability insurance

Commuter benefits and lunch program

Job summary

Lila Sciences is seeking Machine Learning Engineers to develop customer-specific scientific workflows, focusing on training, evaluation, and production‑oriented ML systems. You’ll work at the intersection of AI research, model engineering, and product deployment to turn frontier capabilities into reliable workflows that customers can use in real contexts.

This role bridges research and engineering: training or adapting models, building evaluation loops, debugging model behavior, and

Qualifications

• Experience building, training, adapting, or evaluating machine learning models.
• Strong software engineering skills in Python and ML frameworks (PyTorch, JAX, TensorFlow).
• Experience designing experiments, evaluation metrics, or test sets for model performance.
• Ability to debug model behavior using data, traces, and feedback.
• Experience collaborating across research and engineering teams to deploy ML capabilities.

Responsibilities

• Close the last‑mile gap between model capabilities and customer-specific workflows.
• Post-train models using methods like SFT and RL to align with customer requirements.
• Build evaluation loops to measure model quality and reliability.
• Design experiments to improve model performance across use cases.
• Integrate model behavior into end-to-end product workflows.
• Collaborate with researchers to translate improvements into usable capabilities.

Skills

ML model training

Python programming

Experiment design

Debugging model behavior

Cross-team collaboration

LLMs and multimodal AI

Tools

PyTorch

JAX

TensorFlow

Job description

Lila Sciences is seeking Machine Learning Engineers to develop customer-specific scientific workflows, focusing on training, evaluation, and production‑oriented ML systems. You’ll work at the intersection of AI research, model engineering, and product deployment to turn frontier capabilities into reliable workflows that customers can use in real contexts.

This role bridges research and engineering: training or adapting models, building evaluation loops, debugging model behavior, and