Research Engineer (LLM Performance), London

Posted 3 Days Ago
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London, Greater London, England, GBR
In-Office
Entry level
Biotech
The Role
Research Engineer responsible for optimizing large language model post-training and scaling frontier models for drug discovery. The role involves distributed training and inference performance optimization, fine-tuning and reinforcement learning framework evaluation, bottleneck diagnosis, production system deployment, and low-precision methods. The engineer will collaborate with research and engineering teams across machine learning, computational biology, and chemistry.
Summary Generated by Built In

Isomorphic Labs is applying frontier AI to help unlock deeper scientific insights, faster breakthroughs, and life-changing medicines with an ambition to solve all disease.

The future is coming. A future enabled and enriched by the incredible power of machine learning. A future in which diseases are curtailed or cured starting with better and faster drug discovery. 

Come and be part of an interdisciplinary team driving groundbreaking innovation and play a meaningful role in contributing towards us achieving our ambitious goals, while being a part of an inspiring and collaborative culture.

The world we want tomorrow is the one we’re building today. It starts with the culture at this company. It starts with you. 


About Iso

Isomorphic Labs (IsoLabs) was launched in 2021 to advance human health by building on and beyond the Nobel-winning AlphaFold system. Since then, our interdisciplinary team of drug discovery experts and machine learning specialists has built powerful new predictive and generative AI models that accelerate scientific discovery at digital speed.

Our name comes from the belief that there is an underlying symmetry between biology and information science. By harnessing AI’s powerful capabilities, we can use it to model complex biological phenomena to help design novel molecules, anticipate how drugs will perform and develop innovative medicines to treat and cure some of the world’s most devastating diseases.

We have built a world-leading drug design engine comprising AI models that are capable of working across multiple therapeutic areas and drug modalities. We are continually innovating on model architecture and developing cutting-edge capabilities to advance rational drug design.

Every day, and with each new breakthrough, we’re getting closer to the promise of digital biology, and achieving our ambitious mission to one day solve all disease with the help of AI.


Your impact 

This is an exciting opportunity for you to contribute to frontier research at the intersection of AI and drug design.

Working in a highly creative, iterative environment, you will join the model performance and scaling team, where you will partner with scientists and engineers to scale foundational models that will transform the biopharmaceutical world as we know it.  

You will draw upon your existing engineering experience whilst learning from those around you, to apply novel techniques and ideas to newly encountered model and systems performance optimizations, as well as machine learning, computational biology and chemistry problems.

What you will do 
  • Implement and optimize LLM post-training methods at scale on frontier models.
  • Collaborate with research teams to translate new methods into production-ready systems.
  • Relentlessly prioritize and execute on performance optimization opportunities.
  • Evaluate and deploy frameworks for supervised fine-tuning, reinforcement learning and LLM evaluation.
  • Diagnose and fix performance bottlenecks and communication overhead in distributed training and inference systems. 
  • Deploy low-precision methods to balance performance with accuracy, impacting real world drug design programs.

Skills and qualifications Essential:
  • Significant experience with large scale distributed training of LLMs.
  • Experience with deep learning ML frameworks (either JAX or PyTorch).
  • Knowledge of parallelism strategies and collective communication libraries (e.g. NCCL).
  • Good understanding of GPU architectures. Reasoning about performance concepts is more important than writing kernels from scratch
  • Excellent collaboration skills.
Nice to have:
  • Experience with general LLM serving stacks.
  • Knowledge of XLA, Triton, Pallas, CUDA or similar accelerator DSLs / compilers. 
  • Experience with optimising ML accuracy using low-precision formats.
  • Prior experience building, deploying and maintaining production systems on GCP.
  • Interest in chemistry and biology.

Culture and values

We are guided by our shared values. It's not about finding people who think and act in the same way. These values help to guide our work and will continue to strengthen it. 

Thoughtful
Thoughtful at Iso is about curiosity, creativity and care. It is about good people doing good, rigorous and future-making science every single day.

Brave
Brave at Iso is about fearlessness, but it’s also about initiative and integrity. The scale of the challenge demands nothing less.

Determined
Determined at Iso is the way we pursue our goal. It’s a confidence in our hypothesis, as well as the urgency and agility needed to deliver on it. Because disease won’t wait, so neither should we.

Together
Together at Iso is about connection, collaboration across fields and catalytic relationships. It’s knowing that transformation is a group project, and remembering that what we’re doing will have a real impact on real people everywhere.


Creating an extraordinary company

We believe that to be successful we need a team with a range of skills and talents. We're building an environment where collaboration is fundamental, learning is shared and every employee feels supported and able to thrive. We value unique experiences, knowledge, backgrounds, and perspectives, and harness these qualities to create extraordinary impact.

We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy or related condition (including breastfeeding) or any other basis protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.


Hybrid working

It’s hugely important for us to share knowledge and build strong relationships with each other, and we find it easier to do this if we spend time together in person. This is why we follow a hybrid model, and would require you to be able to come into the office 3 days a week (currently Tuesday, Wednesday, and one other day depending on which team you’re in). If you have additional needs that would prevent you from following this hybrid approach, we’d be happy to talk through these if you’re selected for an initial screening call.

Please note that when you submit an application, your data will be processed in line with our privacy policy.

>> Click to view other open roles at Isomorphic Labs

Skills Required

  • Significant experience with large-scale distributed training of LLMs
  • Experience with deep learning machine learning frameworks such as JAX or PyTorch
  • Knowledge of parallelism strategies and collective communication libraries such as NCCL
  • Good understanding of GPU architectures and performance concepts
  • Excellent collaboration skills
  • Experience with general LLM serving stacks
  • Knowledge of XLA, Triton, Pallas, CUDA, or similar accelerator DSLs or compilers
  • Experience optimizing ML accuracy using low-precision formats
  • Prior experience building, deploying, and maintaining production systems on GCP
  • Interest in chemistry and biology
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The Company
HQ: London
116 Employees
Year Founded: 2021

What We Do

Isomorphic Labs is a new Alphabet company and commercial venture which aims to reimagine the entire drug discovery process from first principles with an AI-first approach, and, ultimately, to model and understand some of the fundamental mechanisms of life. Using computational advances, we’re working at the cutting edge in the new era of ‘digital biology’. By significantly increasing the pace of scientific research and efficacy of new medicines, we will be at the forefront of breakthroughs that will benefit millions of people.

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