Senior / Staff Machine Learning Engineer, Applied AI
$180,000–$336,000 year
On-siteCambridge, Massachusetts, United States
Job Summary
Build evaluation loops to measure model quality and reliability, then design experiments to improve performance across applied customer use cases. Close the gap between Lila AI capabilities and customer-specific scientific workflows by training, adapting, or evaluating machine learning models with Python and frameworks like PyTorch or JAX. Debug model failures using traces and scientific feedback while partnering with researchers and software teams to integrate behaviors into end-to-end product workflows. Create reusable tooling for model adaptation and deployment, feeding customer learnings back into improvement cycles.
Required Qualifications
- 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
Desired Qualifications
- 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
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