382 jobs for “machine learning scientist”
PhysicsX
Owns high-level research workstreams, setting technical direction and roadmaps while guiding junior team members. Translates physics and engineering challenges into mathematical formulations and builds deep learning models to predict physical system behaviour, collaborating with ML and simulation engineers to ship production code.
PyTorch · CUDA · JAX +4
PhysicsX
Lead technical delivery of physics-informed AI, architecting deep learning models and optimisation methods to predict and control physical systems. Own data pipelines and pre-processing best practices while mentoring a team and collaborating with simulation engineers and customers. Involves frequent on-site customer travel across multiple regions.
NumPy · SciPy · Pandas +2
Faculty
Lead complex machine learning delivery and sector strategy within the Defence team, acting as a trusted technical authority. Own project scoping, technical selling and client management while writing production-grade code and mentoring data scientists. Requires eligibility for UK Security Clearance and 2-4 days per week on client sites.
Metaview
Faculty
Lead original technical research in AI safety, steering projects from research question to 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 in PyTorch and Python.
PyTorch · Transformers · Metaview
CrowdStrike · 1001–5000 people · Public
Lead applied research applying LLMs, deep learning and other modern AI techniques to cybersecurity, architecting research strategy and making decisions on model architectures, training and deployment. Build production AI systems at massive scale while mentoring scientists and aligning technical strategy with senior leadership. Distinct for its greenfield, research-to-production focus and thought-leadership expectations.
LLM · Deep Learning Frameworks · GPU +1
CrowdStrike · 1001–5000 people · Public
Lead applied research applying LLMs, deep learning and other modern AI techniques to cybersecurity, architecting research strategy and making decisions on model architectures, training and deployment. Build production AI systems at massive scale while mentoring scientists and aligning technical strategy with senior leadership.
CrowdStrike · LLM · Deep Learning Frameworks +2
Isomorphic Labs · 201–500 people · Series A
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 within an AI-first preclinical team, shaping ADME predictive models and authoring regulatory documents to advance candidates toward first-in-human trials.
Python · R
Isomorphic Labs · 201–500 people · Series A
Lead the safety assessment strategy for drug discovery projects from inception to regulatory submission, designing screening cascades and mechanistic experiments for small molecules and biologics. Partner with machine learning researchers to build predictive toxicology models and manage CRO relationships for outsourced wet-lab data generation, while mentoring junior scientists.
KNIME · Python
Harnham - Data and Analytics Recruitment
Design, build and deploy scalable AI and machine learning solutions from concept to production, including Generative AI applications using LLMs and orchestration frameworks. Write production-grade Python, apply MLOps practices such as CI/CD, containerisation and monitoring, and build data pipelines for training, fine-tuning and inference. Work hybrid in London alongside data scientists, engineers and business stakeholders on large-scale datasets.
LLMs · Orchestration Frameworks
Paid, fixed-term research residency of six weeks to six months for people transitioning into empirical AI safety work, with a scoped project and direct mentorship from Anthropic researchers. Residents get Claude model and GPU compute access and work on alignment, interpretability, scalable oversight, red-teaming or ML systems alongside research teams.
Kubernetes · LLMs · Claude +1
NVIDIA · Public
Research and develop techniques to GPU-accelerate leading high performance computing applications across scientific computing, computational engineering and data science. The intern performs in-depth analysis and optimisation for current and next-generation GPU architectures, working with application developers and collaborating with architecture, research, libraries, tools and system software teams.
CUDA · OpenACC · GPU
NVIDIA · Public
Research and develop techniques to GPU-accelerate leading high performance computing applications across scientific computing, computational engineering and data science. The intern performs in-depth analysis and optimisation for current and next-generation GPU architectures, working directly with application developers and collaborating with architecture, research, libraries, tools and system software teams.
CUDA · OpenACC · GPU
Relation Therapeutics
Lead the platform engineering team owning hybrid on-prem and cloud compute, Kubernetes clusters, MLOps infrastructure and scientific compute environments for machine learning and drug discovery. Set architectural strategy, mature CI/CD and observability, and own platform security and data governance for sensitive patient-derived data, reporting to the VP of Engineering.
Kubernetes · Kubeflow · AWS +4
UCL
Lead an independent research programme applying multimodal AI and machine learning to digital pathology and electronic health record data, working with large-scale clinical datasets to advance translational cancer research. The role sits within a collaborative cancer institute and involves close partnership with UCLH's SAFEHR programme, publishing high-impact work, securing external funding, and mentoring junior researchers.
University of Glasgow
Develops physics-informed machine learning and uncertainty quantification methods to improve subsurface characterisation for offshore wind and net-zero infrastructure. Joins an interdisciplinary team of engineers, data scientists and geophysicists within a UKRI-funded Fellowship, with scope to shape an ambitious research programme.
