Machine Learning Jobs

• London
• On-site
• Senior
• Permanent

Builds generative and predictive models of cellular behaviour from high-content imaging, curating cell painting and brightfield datasets and designing evaluation that separates biological signal from technical artefact. Partners closely with ML scientists and wet lab assay teams, linking imaging readouts to single-cell and perturbation data in a matrixed TechBio environment.

NumPy · pandas · Polars +6

• London
• Senior
• Permanent

Processes and QC's paired single-cell RNA-seq and scATAC-seq at scale, building Nextflow pipelines and multimodal training datasets in Zarr for GPU-accelerated model training. Partners with wet lab scientists and ML modellers, contributing to evaluation frameworks that test whether models capture meaningful biology.

STARsolo · Alevin-fry · Chromap +7

• Cambridge, United States
• Hybrid · 3 days in office
• Senior · manages people

Lead computational drug design programs from hit identification through to candidate nomination, applying AI-first approaches and shaping the ML drug design engine alongside ML scientists. Roles span Program Lead or Computational Chemistry Lead, with some positions managing 2-3 computational chemists. Hybrid working in Cambridge, MA, three days a week in office.

REINVENT

• Cambridge, United States
• Hybrid · 3 days in office
• Lead

Lead DMPK strategy across drug discovery projects, designing screening cascades and mechanistic experiments while integrating PK, PD and toxicology data for dose and safety predictions. Work with AI/ML platform teams to optimise ADME models and author regulatory DMPK sections, in a hybrid Cambridge, MA role.

Python · R

• London
• Hybrid · 1 day in office
• £260,000 – £630,000 pa
• Mid-level

Join the Pretraining team to develop next-generation large language models, working across model architecture, algorithms, data processing and optimizer development. You'll lead small research projects, design and analyse experiments, and scale distributed training infrastructure to thousands of GPUs, blending research and engineering in a highly collaborative environment.

PyTorch · Kubernetes · GPUs +1

• London
• On-site
• £260,000 – £630,000 pa
• Mid-level

Own critical parts of a production pretraining pipeline, covering performance optimization, observability, reliability and hardware debugging across the full stack. The role splits roughly 50/50 between research and engineering, with on-call incident response during model launches and close collaboration with teams in SF and London.

JAX · TPU · PyTorch +3

• United Kingdom
• Remote
• Permanent

Owns the path from research checkpoint to production serving infrastructure, optimising inference latency, throughput and cost with quantization, distillation, KV-cache tuning, batching and custom kernels. Builds high-performance streaming serving systems and tooling so researchers can ship models quickly and safely.

CUDA · Triton · TensorRT +2

• United Kingdom
• Remote
• Mid-level
• Permanent

Owns large-scale distributed web crawling systems that discover, fetch and extract data from billions of pages to feed frontier AI models. Work spans content extraction from messy HTML, deduplication at web scale, recrawl and freshness strategies, politeness and rate limiting, plus tooling for researchers to request and explore crawled data.

Kubernetes

• London
• Hybrid
• Senior · manages people

Owns research work-streams, setting technical direction and roadmaps while guiding junior team members. Builds deep learning and probabilistic models of physical systems, translating physics and engineering problems into mathematical formulations and production-ready code with ML and simulation engineers.

PyTorch · CUDA · JAX +2

• London
• Hybrid
• Lead
• Permanent

Lead an ambitious research agenda at the intersection of LLMs, VLMs, VLAs and embodied AI, exploring how world knowledge, memory and reasoning can drive better physical-world decisions. Stay hands-on developing architectures, training and post-training large-scale models, while mentoring researchers and shaping the team's technical direction across London and Sunnyvale.

PyTorch

• Oxford
• On-site
• £40,178 – £48,700 pa
• Entry level
• Contract

Develops and validates machine-learning models on large-scale multimodal NHS clinical data, including electronic health records, genomics, imaging and digital pathology. Builds reproducible pipelines for heterogeneous longitudinal data within secure environments, working with clinicians, pathologists and engineers on disease progression and treatment response prediction.

NHS Data · Electronic Health Records · Secure Data Environment +2

• London
• Hybrid
• Senior
• Permanent

Lead original technical research in AI safety, steering projects from initial question to high-impact publication. Work spans interpretability, steering vectors, goal drift, machine unlearning and uncertainty calibration for transformer LLMs and agentic systems, with outputs informing frontier labs and government institutes. Involves building reproducible research code and collaborating with red-teaming and evaluation teams.

PyTorch · Transformer · Metaview

• London
• Hybrid
• Lead · manages people
• Permanent

Leads a small, high-agency research team setting the scientific agenda for AI safety, with a focus on large language models and safety-critical systems. Involves conducting and overseeing red-teaming and evaluations in high-risk domains such as CBRN and cybersecurity, publishing at top conferences, and mentoring researchers while aligning work with commercial goals.

Python · OpenAI o1 · Metaview

• Oxford
• On-site
• £39,424 – £47,779 pa
• Entry level
• Contract

Develops novel statistical and machine learning methods for large-scale genomic, phenotypic and health data, covering areas such as genomic language models, multimodal and federated learning, and multi-ancestry methods. The role involves building scalable, reproducible research software, applying it to biobank-scale datasets, publishing findings and guiding junior researchers within the Palamara Group.

• Cambridge
• On-site
• Permanent

Consultants design and deploy machine learning systems for diverse defence and security projects, from proofs-of-concept to working prototypes. The role spans literature analysis, model training, rapid prototyping and experiment design, often combining science, engineering and ML in novel settings. Work is on-site in Cambridge within a flat, collaborative team of around 40 specialists.

PyTorch · TensorFlow · Keras +3