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Salary: USD 252,000 - 336,000 / annual
Lila Sciences is seeking a Principal Machine Learning Engineer to join the Applied AI organization. This is a high-impact senior individual contributor role focused on bridging the gap between frontier AI model capabilities and customer-specific scientific workflows.
You will lead post-training efforts using techniques such as supervised fine-tuning (SFT) and reinforcement learning (DPO, PPO, GRPO) to align model behavior with customer requirements. You'll build and operate evaluation loops that measure model quality, reliability, and customer fit, translating learnings into concrete model improvement cycles. The role involves close partnership with AI researchers to translate model advances into reliable, usable capabilities, and with software engineers to integrate model behavior into end-to-end product workflows.
Key responsibilities include:
- Applying deep hands-on expertise to close gaps between model capabilities and customer-specific scientific workflows
- Leading post-training initiatives using SFT and RL methods to align model behavior with requirements and feedback
- Building and running evaluation systems that measure quality, reliability, and customer fit
- Turning customer learnings, data signals, and evaluation results into model improvement cycles
- Partnering with AI researchers and software teams to translate advances into reliable capabilities
- Debugging complex model failures using traces, evaluations, customer context, and scientific feedback
- Mentoring engineers and sharing reusable patterns for model adaptation, evaluation, and deployment
Lila Sciences is building Scientific Superintelligence to solve major challenges through AI. The company combines advanced AI models with proprietary AI Science Factory instruments to create an operating system for science that executes the scientific method autonomously, accelerating discovery across medicine, materials, and energy.
REQUIREMENTS:
- Minimum 2–3 years of hands-on post-training experience (SFT, RL methods such as DPO/PPO/GRPO) and evaluation system design
- Strong software engineering skills in Python and modern ML frameworks like PyTorch
- Demonstrated ability to debug ambiguous, high-stakes model behavior quickly using data, traces, logs, and qualitative feedback
- Experience leading technical work across research and engineering teams
- Deep familiarity with large language models, multi-modal models, or agentic AI systems
- Clear communication skills for translating customer needs into technical approaches and explaining complex model behavior to technical and non-technical audiences
BONUS:
- Experience adapting models for customer-facing or production workflows in scientific, technical, or data-intensive domains
- Experience with RL post-training (RLHF, GRPO, tool-augmented RL)
- Experience building evaluation harnesses, model monitoring, or quality dashboards
- Experience training MoE architectures
- Track record of mentoring engineers and being sought out as a go-to technical expert
