Position SummaryWe are rebuilding the Design-Make-Test-Analyze (DMTA) cycle, infusing scientific automation with foundation models, multi-agent systems, and robotics to make scientific discovery intelligent, autonomous, and fast.
We're seeking a scientist-engineer hybrid to design the
learning layer of our scientific agent platform. You will design the environments, rewards, and domain-specific models that enable agents to improve based on experimental feedback. You'll translate wet-lab and computational endpoints into a trainable signal to build models that plan and act against them.
Responsibilities:Research & Innovation- Partner with scientists to build autonomous agents that undertake molecule discovery tasks
- Design and build reinforcement learning (RL) environments that wrap real discovery tasks with appropriate state, action, and termination semantics.
- Curate and engineer reward functions from noisy scientific signal.
- Post-train domain models (SFT, DPO/GRPO/PPO, reward modeling, distillation) on chemistry and biology tasks
- Integrate learned policies with domain tools (RDKit, molecular graph ML, ELN/LIMS APIs, instrument drivers) so trained models execute real DMTA tasks
- Build the eval infrastructure: task suites, scoring harnesses, regression tracking, and experiment tracking (e.g., MLflow)
External Engagement- Represent Frontier AI in the broader AI[redacted] and external AI research community: publish, give talks, review papers, and scout emerging trends.
- Evaluate external vendors, open-source projects, and academic collaborations for strategic fit.
What Success Looks Like- Trained models that measurably outperform prompted frontier baseline models on internal discovery tasks
- Reward and evaluation infrastructure that other teams adopt as the default way to measure agent performance
- Measurable reduction in DMTA turnaround through autonomous planning and execution
- Seamless transition from prototype to production-deployed AI systems
Basic Qualifications:- PhD (or MS + 3 yrs / BS + 5 yrs equivalent experience) in Machine Learning, Bioinformatics, Cheminformatics, Computer Science, or related discipline with demonstrated wet-lab collaboration or hands-on experience.
- Approximately 1-2 years of demonstrated experience in applying AI/ML in scientific disciplines such as biology, chemistry, neuroscience, or a related field (industry postdoc counts)
- Hands-on experience training or post-training AI models
Additional Preferences:- Proficiency in Python and deep experience with ML/Deep Learning frameworks (e.g., PyTorch, Tensorflow, JAX, HuggingFace).
- Experience with RL and post-training methods (PPO, GRPO, DPO, reward modeling, RLHF/RLAIF) and libraries such as TRL, verl, or equivalent in-house stacks
- Familiarity with molecular representation learning, generative chemistry, or protein/nucleic acid models
- Hands-on experience building agentic AI systems (e.g., OpenAI/ Anthropic Agent SDK, Langchain, Smol agents)
- Experience designing and shipping end-to-end systems in cloud environments (backend APIs, lightweight frontends, and agentic platforms) - GitHub portfolio a plus
- Working knowledge of cloud-native (AWS/Azure) pipeline architectures, including Nextflow, Argo on Kubernetes
- Demonstrable research experience, evidenced by contributions to projects, and ideally through publications in relevant ML/NLP venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP).
- Experience mentoring and guiding junior researchers or engineers.
Actual compensation will depend on a candidate's education, experience, skills, and geographic location. The anticipated wage for this position is
$151,500 - $244,200
Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly's compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.
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