Senior Software Engineer, ML Ops

Confirmed live in the last 24 hours

Isomorphic Labs

London

Hybrid

Posted April 20, 2026

Job Description

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.

Senior/ Principal Software Engineer, ML Ops

Your Impact

We are building the largest foundation models in biotech and applying them immediately to cure disease. You will play a pivotal role in ensuring the reliability and scalability of the foundations that make this possible.

As a Principal Engineer, you will lead the efforts to harden our systems, ensuring our groundbreaking AI is built on an unshakeable base, working closely with the research team and the Applied ML teams to ensure the infrastructure is stable, reliable and can operate with more data and larger models as we grow.

What You Will Do

Develop and operate our inference platform, serving fleets of cutting-edge machine learning models to scientific applications.
Design strategy and build roadmaps for maturing the platform.
Architect and optimize our next-generation inference services. You will solve core scaling limits, ensuring high-throughput performance and feature parity across our model serving stack.
Contribute to core technical decisions on tooling and architectural design while partnering with science, product, and operations teams to align infrastructure with biotech R&D cycles.
Deliver high-quality and well-tested user-focused features.

Skills and Qualifications

Essential:

Proven experience in architecting and managing large-scale AI/ML workloads in a production environment.
Expertise in cloud compute design, specifically within Google Cloud Platform (GCP).
Orchestration: Significant experience deploying and managing complex workloads within Kubernetes (GKE).
Professional familiarity with NVIDIA GPU generations and the intricacies of high-performance compute.
Strong programming skills and a "reliability-first" approach to software development.

Nice to Have:

A career history that spans both ML Software Engineering and Infrastructure SRE roles.
Experience leading multi-disciplinary projects and navigating complex stakeholder requirements in a fast-paced environment.
Familiarity with workload scheduling, ML efficiency research, and hardware benchmarking.
Experience with Google TPU generations

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