Senior / Staff Machine Learning Engineer (Applied AI)

TL;DR

Senior / Staff Machine Learning Engineer (Applied AI): Training, adapting, and evaluating Lila’s AI models to close the last-mile gap to customer-specific scientific workflows with an accent on building reliable evaluation loops, debugging model behavior end to end, and iterating production-oriented ML systems. Focus on designing experiments, translating improvements into usable capabilities, and integrating model behavior into real customer contexts.

Location: Cambridge, MA, USA; San Francisco, CA, USA

Company

hirify.global builds AI systems for autonomous scientific discovery across medicine, materials, and energy.

What you will do

• Turn Lila AI model capabilities into customer-specific scientific workflows by bridging research and engineering.
• Build evaluation loops to measure model quality, reliability, and customer fit.
• Design experiments and improve model performance across applied customer use cases.
• Debug model failures using traces, evaluations, customer context, and scientific feedback.
• Collaborate with AI researchers and Software to integrate model behavior into end-to-end product workflows.
• Create reusable tooling for model adaptation, evaluation, and deployment workflows.

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.

Nice to have

• Experience adapting models for customer-facing or production workflows.
• Experience building evaluation harnesses, model monitoring, or quality dashboards.
• Familiarity with retrieval-augmented generation, tool use, or agentic workflows.
• Experience with RL post-training (e.g., RLHF, GRPO, tool-augmented RL) and/or training MoE architectures.
• Experience with scientific, technical, or data-intensive customer use cases.

Culture & Benefits

• Early-team autonomy with flexibility and compute to tackle frontier science problems.
• Close collaboration between Applied AI, AI Research, and Software to move capabilities into production-quality systems.
• Focus on iterative improvement using customer learnings, data signals, and evaluation results.

Hiring process

• Interviews focused on ML engineering experience, evaluation/experimentation approach, and cross-team collaboration.
• Discussion of how to translate customer needs into technical model improvements and production workflows.