Back to jobs

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

UK Office · Bristol, England, United Kingdom

Live, first seen by us 67 days ago
Pay
Pay not listed
Work setting
Working pattern needs review — work setting source
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
Location type: Hybrid
From the employer’s posting
Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting
Team
Technology › Machine Learning — team source
Department: Technology › Machine Learning
From the employer’s posting

What you’ll work on

Full posting
  • Research and develop post-training strategies for multimodal foundation models

  • Design reward functions, training objectives, and evaluation protocols for RL methods

  • Build experimentation and hyperparameter optimization workflows

  • Develop inference optimization for high-throughput evaluation and interactive discovery

  • Build benchmarking and evaluation frameworks across modalities and scientific use cases

From the employer’s posting
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

What you’ll bring

All qualifications

Core experience

  • Strong Python and PyTorch skills, including implementing, training, debugging and evaluating deep learning models end-to-end
  • Experience with multimodal or multi-task model architectures
  • Demonstrated experience training large-scale transformer models
  • Familiarity with biomedical, chemical, or biological data domains
  • Demonstrated experience in one or more of the following:
Qualification wording
Strong Python and PyTorch skills, including implementing, training, debugging and evaluating deep learning models end-to-end
Experience with multimodal or multi-task model architectures
Demonstrated experience training large-scale transformer models
Familiarity with biomedical, chemical, or biological data domains
Demonstrated experience in one or more of the following:
Education & alternatives
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
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

About Iambic Therapeutics

Our mission is to deliver better medicines through innovations in AI-based discovery technologies.

In the employer’s words · Read in context · Company website ↗

Apply at Iambic Therapeutics

Posting record

Report an error
Status in our records
Live
First seen by us
Aug 29, 202667 days ago
Recorded sightings
47
Last seen by us
Nov 4, 2026
Employer says posted
Aug 27, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 67

Similar jobs usually come down between day 6 and day 69.

From 4,362 similar postings that came down. Coming down is not always a hire.

Iambic Therapeutics is hiring

5 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Takes jobs down at a usual pace
Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • Docker
  • Kubernetes
  • PyTorch
Where the posting names them
- 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
- 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

Your next step

Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.

Applying through Ashby

  • No account needed. “Autofill from resume” at the top of the form fills the key fields from your file.

Already applied? Track this application

Has Iambic Therapeutics posted this job before?

Yes. We’ve followed one other Machine Learning Scientist — Large Multimodal Models (Post-Training) job there since August 2026. It is still up.

  1. BostonStill up after 68 days, first seen Aug 29
  2. This jobUp 68 days so far, first seen Aug 29

About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View 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

Iambic is a clinical-stage life-science and technology company developing novel medicines using its AI-driven discovery and development platform. Based in San Diego and founded in 2020, Iambic has assembled a world-class team that unites pioneering AI experts and experienced drug hunters. The Iambic platform has demonstrated delivery of new drug candidates to human clinical trials with unprecedented speed and across multiple target classes and mechanisms of action. Iambic is advancing a pipeline of potential best-in-class and first-in-class clinical assets, both internally and in partnership, to address urgent unmet patient need. Learn more about the Iambic team, platform, pipeline, and partnerships at iambic.ai.

MISSION & CORE VALUES

Our mission is to deliver better medicines through innovations in AI-based discovery technologies. The culture and work at Iambic Therapeutics are profoundly strengthened by the diversity of our people and our differences in background, culture, national origin, religion, sexual orientation, and life experiences. We are committed to building an inclusive environment where a diverse group of talented humans work together to discover therapeutics and create technologies.

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.

Source details

View original posting ↗

Source notes

Source excerpts

Selected passages from the saved posting. Check the full description for conditions and exceptions.

Pay

No pay amount identified in the saved description.

Location & working pattern

Bristol, England, United Kingdom

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
Work authorization

No clear work-authorization passage found. Eligibility is unconfirmed.

Find answers in the posting

AI
How answers work

AI selects complete passages from this posting. Check them for conditions and exceptions.

Uses this posting and your question. No profile needed.