Multimodal ML Scientist for Drug Discovery (Remote)

Iambic; Boston (MA); Remote; USD 140,000 - 210,000; Full time

Benefits offered by this job

Competitive pay

Company paid healthcare

Flexible spending accounts

Voluntary life insurance

401K matching

Uncapped vacation

Job summary

Iambic Therapeutics is seeking a Machine Learning Scientist to advance post-training methods for multimodal foundation models used in drug discovery. The role collaborates across ML, software, and biology teams to push the capabilities of AI-driven discovery technologies.

Eligible candidates range from recent PhDs to seasoned researchers. Remote work is offered with an option to be on-site in Boston. Strong publication or deployment records are valued.

Qualifications

• PhD or equivalent industry experience demonstrating deep expertise in ML.
• Strong Python and PyTorch skills, including implementing, training, debugging and evaluating deep learning models end-to-end.
• Experience training large-scale transformer models.
• Experience with reinforcement learning approaches such as RLHF, RLAIF, PPO, GRPO, RL with verifiable rewards.
• Experience with supervised fine-tuning, full-parameter fine-tuning, or LoRA.
• Systematic hyperparameter optimization or large-scale experimentation (Optuna, Ray Tune).
• Strong engineering practices: reproducible experimentation, clean code, testing, and performance-aware debugging.
• Comfort with ML infra (Docker, CUDA, Kubernetes, Weights & Biases).

Responsibilities

• Research and develop post-training strategies for large-scale multimodal foundation models.
• Design reward functions, training objectives, data-generation strategies, and evaluation protocols for RL and other post-training methods.
• Build systematic experimentation and hyperparameter optimization workflows for exploring post-training recipes, model configurations, and training strategies.
• Develop and apply inference optimization techniques to support deployment in high-throughput evaluation and interactive discovery workflows.
• Design and maintain rigorous benchmarking and evaluation frameworks across modalities and downstream tasks.
• Collaborate with ML and software engineering teams to productionize models, evaluation systems, and inference services.
• Partner with chemists and biologists to ground model objectives in drug discovery needs.
• Communicate results to internal teams and external partners; present at conferences.
• Write high-quality research and engineering code: refactor, test, document, and package ML components.

Skills

Python

PyTorch

Reinforcement learning

Large-scale transformers

Supervised fine-tuning

LoRA / parameter-efficient tuning

Hyperparameter optimization

Clean code & reproducible experiments

Docker

CUDA

Kubernetes

Weights & Biases

Education

PhD in ML/CS/ computational chemistry/ physics or equivalent

Tools

Optuna

Ray Tune

Weights & Biases

Docker

Kubernetes

CUDA

Job description

Iambic Therapeutics is seeking a Machine Learning Scientist to advance post-training methods for multimodal foundation models used in drug discovery. The role collaborates across ML, software, and biology teams to push the capabilities of AI-driven discovery technologies.

Eligible candidates range from recent PhDs to seasoned researchers. Remote work is offered with an option to be on-site in Boston. Strong publication or deployment records are valued.