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Salary: USD 180,000 - 298,000 / annual
Lila Sciences is seeking a Senior Machine Learning Engineer for its Applied AI organization. This role sits at the intersection of AI research, model engineering, and product deployment, focusing on adapting Lila's frontier AI models to customer-specific scientific workflows.
You will own hard technical problems in model post-training and evaluation, working hands-on to close the gap between model capabilities and real-world customer needs. Key responsibilities include:
- Apply expertise in post-training techniques (SFT, RL methods like DPO, PPO, GRPO) to align model behavior with customer requirements
- Design and run evaluation loops to measure model quality, reliability, and customer fit
- Debug complex model failures using traces, evaluations, customer context, and scientific feedback
- Partner with AI researchers to translate model advances into reliable, production-ready capabilities
- Collaborate with software teams to integrate model behavior into end-to-end product workflows
- Turn customer learnings and data signals into concrete model improvement cycles
This is an individual contributor role for someone who wants to go deep on model adaptation and evaluation, take real ownership of technical challenges, and grow into broader scope over time. You will be sought out as a go-to technical expert and may mentor other engineers, but this is not a people-management position.
Lila Sciences is building Scientific Superintelligence to solve major challenges in medicine, materials, and energy. The company combines advanced AI models with proprietary AI Science Factory instruments to execute the scientific method autonomously.
REQUIREMENTS:
- Minimum 1–2 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
- 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 a go-to technical expert
