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Machine Learning Scientist — Large Multimodal Models (Post-Training)

Join Iambic Therapeutics as a Machine Learning Scientist to innovate AI-based drug discovery with multimodal models.

Location
UK Office
Compensation
Not disclosed
Level
mid
Type
full time · Hybrid

Posted by employer 2 months ago

First seen on Joblaze 1 month ago

Last verified on the company career page 9 hours ago

AI in the day-to-day

The role involves researching and developing AI-based discovery technologies for drug development.

Requirements

Education
PhD

Not disclosed in this posting: compensation, years of experience, visa sponsorship.

Benefits

Pension Contributions Flexible Holiday Allowances Health Insurance

Joblaze summary

In this role, the Machine Learning Scientist focuses on developing and evaluating post-training strategies for large multimodal transformer models aimed at enhancing drug discovery. Key skills include proficiency in Python and PyTorch, along with experience in reinforcement learning and systematic experimentation. This position is suitable for candidates ranging from recent PhD graduates to seasoned researchers with a strong publication record. Iambic Therapeutics fosters a collaborative environment, integrating AI expertise with drug development to address critical healthcare needs.

Joblaze insights

  • Listed about a month ago — first seen on Joblaze September 3, 2026. Last confirmed on Iambic Therapeutics's careers page October 27, 2026.
  • This exact title is also open at 1 other location at Iambic Therapeutics: Boston Office.
  • Python appears in 42.6% of 54 comparable mid ai/ml roles in United Kingdom; Docker appears in 3.7% of 54 comparable mid ai/ml roles in United Kingdom.

Quick facts

Is the Machine Learning Scientist — Large Multimodal Models (Post-Training) role remote?
It's hybrid — Iambic Therapeutics expects some on-site time in UK Office.
Where is the role based?
Iambic Therapeutics is hiring for this position in UK Office.
What's the tech stack?
Joblaze extracted these technologies from the posting: CUDA, Docker, Hyperparameter Optimization, Kubernetes, PyTorch, Python.
What seniority level is this role?
Iambic Therapeutics targets mid-level candidates for this position.
Is this full-time or contract?
Full-time for this Machine Learning Scientist — Large Multimodal Models (Post-Training) role at Iambic Therapeutics.

From the original posting

JOB SUMMARY

We are seeking a Machine Learning Scientist to join the Enchant team at Iambic Therapeutics. Our mission is to deliver better medicines through innovation in AI-based discovery technologies. In this role, you will research and develop post-training methods for Enchant - our multimodal transformer model trained on a wide variety of biomedical data - pushing the boundaries of what large-scale foundation models can achieve in drug discovery.

The role centers on designing and evaluating post-training approaches for large multimodal language models including supervised fine-tuning, parameter-efficient fine-tuning, reinforcement learning, preference or reward-based optimization, and other emerging post-training methods. You will develop rigorous evaluations and training infrastructure that make it possible to rapidly iterate on these approaches at scale and work closely with colleagues across machine learning, software engineering, and drug discovery to put powerful foundation models into the hands of scientists making real therapeutic decisions.

We are hiring across multiple levels and welcome candidates ranging from recent PhD graduates to experienced researchers with a strong publication or deployment record. This is a remote position, with the option to be on-site in our Bristol office.

KEY 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 reinforcement learning and other post-training approaches applied to multimodal LLMs

  • Build systematic experimentation and hyperparameter optimization workflows to efficiently explore post-training recipes, model configurations, and training strategies

  • Develop and apply inference optimization techniques to support deployment in both high-throughput model evaluation and interactive discovery workflows

  • Design and maintain rigorous benchmarking and evaluation frameworks that measure model quality across modalities, downstream tasks, and scientific use cases

  • Collaborate with ML and software engineering colleagues to productionize models, evaluation systems, and inference services

  • Partner with computational chemists, medicinal chemists, and biologists to ensure model development and post-training objectives are grounded in drug discovery needs

  • Communicate results to internal teams, external partners, and at conferences

  • Write high-quality research and engineering code: refactor, test, document, and package ML components to support team velocity

QUALIFICATIONS

  • PhD in machine learning, computer science, computational chemistry, physics, or a related computational STEM field, or equivalent industry experience demonstrating comparable depth

  • Strong Python and PyTorch skills, including implementing, training, debugging and evaluating deep learning models end-to-end

  • Demonstrated experience training large-scale transformer models

  • Demonstrated experience in one or more of the following:

  • Reinforcement learning approaches such as RLHF, RLAIF, PPO, GRPO, RL with verifiable rewards, or related methods (strongly preferred)

  • Supervised fine-tuning, full-parameter fine-tuning, parameter-efficient fine-tuning (LoRA), or related methods

  • Systematic hyperparameter optimization or large-scale experimentation using tools such as Optuna, Ray Tune, or similar frameworks

  • Strong engineering practices: reproducible experimentation, clean code, testing, and performance-aware debugging

  • Comfort with modern ML infrastructure (e.g., Docker, CUDA, Kubernetes, experiment tracking tools such as Weights & Biases)

PREFERRED

  • Experience with multimodal or multi-task model architectures

  • Training and inference optimization (e.g., mixed precision, kernel optimization, quantization, distributed strategies)

  • Familiarity with biomedical, chemical, or biological data domains

  • Distributed training at scale

  • HPC or large-scale training operations experience

ABOUT IAMBIC THERAPEUTICS

MISSION & CORE VALUES

PAY AND BENEFITS

We offer a competitive compensation package, private medical insurance, life assurance, pension contributions, and flexible holiday allowances to our team. Our UK office provides a modern and collaborative work environment, right in the centre of Bristol.

Standard company text repeated across Iambic Therapeutics's postings is omitted here.

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