Top Tech Jobs & Startup Jobs in London

Design, operate, and extend scheduling systems for large-scale machine learning workloads on Kubernetes. Optimize GPU and TPU utilization through gang scheduling, topology-aware placement, and preemption. Build custom Kubernetes controllers, operators, and CRDs; ensure reliability through operational processes, runbooks, and SLOs; and collaborate with ML researchers and infrastructure teams to support efficient batch inference and training.

Lead the Platform group in designing, operating, and improving cloud infrastructure for research, development, production, and machine-learning training and inference. Own Kubernetes, infrastructure as code, deployment tooling, monitoring, automation, software lifecycle practices, and architectural decisions. Collaborate with engineers, scientists, ML researchers, product, business development, and operations teams to deliver reliable infrastructure in a fast-moving environment.

Leads corporate development and inorganic growth initiatives across digital biology, biotechnology, and enabling technologies. Responsibilities include market landscaping, scientific and commercial opportunity evaluation, business case development, valuation modeling, deal structuring, negotiation, due diligence, transaction closing, strategic investments, executive recommendations, pipeline governance, and cross-functional coordination with R&D, machine learning, legal, finance, and commercial teams.

Build and evolve large-scale machine learning infrastructure for distributed training and inference workloads. The role involves improving platform reliability, deploying research-supporting agents, enabling scalable ML development, and partnering with engineering, research, and product teams. Strong Python and large-scale infrastructure experience are essential; Kubernetes, GCP, internal platform development, and multi-cloud experience are advantageous. The position follows a hybrid model requiring three office days per week in London.

Design and operate large-scale compute infrastructure for AI and machine learning workloads. Build observability and telemetry systems, improve GPU efficiency and workload goodput, integrate accelerator hardware, maintain Kubernetes and cloud clusters, and develop automated recovery and reliability mechanisms. Collaborate with ML infrastructure, research, platform, and business teams while contributing to architectural and tooling decisions.

Operating Partner supporting AI leadership by advising executives, prioritizing strategic initiatives, coordinating cross-functional teams, translating complex technical topics into executive communications, driving execution, and improving organizational efficiency. The role partners across research, engineering, product, and operations to deliver high-priority initiatives and support organizational scaling.

The Principal Software Engineer will ensure the reliability and scalability of AI systems, leading infrastructure improvements and optimizing ML workflows.

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.

Design and develop novel machine learning models and training algorithms for drug discovery. Lead and run research projects, implement and scale training/inference frameworks, analyze experiments, and collaborate across disciplines. Mentor and provide technical guidance to other ML researchers and contribute to research direction and presentations.

As a Research Engineer, you will develop and optimize AI models for drug discovery, collaborating with scientists to advance ML applications in biopharmaceuticals.