Machine Learning Performance Engineer, Isomorphic Labs
More information about location
3+ days a week in office
AI-driven drug discovery and development
Open for applications
AI-driven drug discovery and development
201-500 employees
Open for applications
More information about location
3+ days a week in office
201-500 employees
Company mission
To use AI and machine learning methods to accelerate and improve the drug discovery process.
Role
Who you are
- Strong working knowledge of HPC and ML Systems
- Good understanding of GPU and other AI accelerator architectures
- Strong knowledge of data structures and algorithms
- Good working knowledge of maths / linear algebra
- Experience with deep learning ML frameworks (preferably JAX)
- Excellent collaboration skills
Desirable
- Knowledge of XLA, Triton, CUDA, Pallas or similar accelerator DSLs / compiler stacks
- Experience with distributed training and data/model sharding strategies
- Knowledge of collective communication libraries (e.g. NCCL)
- Proven ability to optimise ML accuracy with low-precision formats
- Experience building, deploying and maintaining production systems on GCP
- Interest in chemistry and biology
What the job involves
- We are looking for engineers with different levels of experience - Mid through to Senior, Staff or equivalent levels
- This is an exciting opportunity for you to shape the new performance and scaling focused capability of Isomorphic Labs
- Develop custom GPU kernels maximising utilisation and application performance
- Help design, implement, and optimize advanced distributed training and inference strategies
- Implement low-level optimisations to overcome hardware limitations
- Design low-precision algorithms balancing high performance with minimal accuracy loss
- Use your intuitions of both runtime and hardware to optimize performance for both latency and throughput on real world drug design programs
- Partner with technical infrastructure teams to deploy solutions and manage training uptime
- Work with ML engineers and researchers to create efficient training and inference model architectures
Salary benchmarks
Our take
The discovery of new drugs is essential for medical innovations, but the process is lengthy, high-risk, and expensive. This is due to manual research and analysis processes that are prone to human error and time-consuming workflows, raising the question as to whether AI could reduce these pitfalls.
Rather than bolting machine learning onto the existing drug discovery process, Isomorphic Labs is reimagining everything from first principles with an AI-first approach, leveraging machine learning to build predictive and generative models of biological phenomena to understand how novel drugs could perform. And the company differentiates itself from other AI-driven drug discovery startups by being a direct subsidiary of Alphabet, with access to resources, computing power, and partnerships with teams within DeepMind.
Now, the company is gearing up for its biggest leap yet - testing its AI-designed medicines on real people. After years of development, big-name partnerships, and a $600M funding round, Isomorphic says human clinical trials are finally within reach. In its London offices, scientists and AI are already teaming up to design potential treatments for diseases like cancer. The next step is putting those drugs to the test.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
30% female employees
Company
Funding (1 round)
Mar 2025
$600m
LATE VC
Total funding: $600m
Company values
- 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
Company HQ
London, UK
Articles
Leadership
Demis Hassabis
(Co-Founer & CEO)Co-founded Google DeepMind in 2010 and has served as the CEo ever since.
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