1,728 Machine Learning Models jobs in the United Kingdom

• Conducting research and development on novel machine learning algorithms and deep learning architectures.
• Designing, implementing, and training AI models for various applications, including but not limited to natural language processing, computer vision, and predictive analytics.
• Optimizing AI models for performance, scalability, and efficiency.
• Collaborating with cross-functional teams to integrate AI solutions into existing products and platforms.
• Evaluating and benchmarking AI model performance against state-of-the-art methods.
• Staying abreast of the latest advancements in AI, machine learning, and related fields through literature review and conference participation.
• Developing proof-of-concepts and prototypes to demonstrate the feasibility of new AI approaches.
• Contributing to the company's intellectual property through patents and publications.
• Mentoring junior engineers and contributing to a culture of innovation and technical excellence.

• Conduct cutting-edge research in machine learning, deep learning, and related AI fields.
• Design, develop, and implement novel ML algorithms and models for various applications.
• Perform rigorous experimentation, evaluation, and validation of AI models.
• Collaborate with engineering teams to deploy research prototypes into production environments.
• Stay abreast of the latest advancements in AI research and literature.
• Publish research findings in top-tier conferences and journals.
• Mentor junior researchers and engineers, fostering a culture of scientific inquiry.
• Contribute to the strategic direction of the company's AI research roadmap.
• Develop and maintain high-quality code for AI research and development.
• Effectively communicate complex research concepts to both technical and non-technical audiences.

• PhD or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
• Several years of post-graduate research experience in AI/ML.
• Demonstrated expertise in one or more areas of machine learning, such as deep learning, reinforcement learning, NLP, computer vision, etc.
• Proficiency in programming languages such as Python, and ML frameworks like TensorFlow, PyTorch.
• Strong publication record in leading AI conferences (e.g., NeurIPS, ICML, ICLR) or journals.
• Excellent analytical, mathematical, and problem-solving skills.
• Experience with large-scale data processing and distributed computing is a plus.
• Strong communication and collaboration skills, with the ability to work effectively in a remote team.
• Ability to work independently and drive research projects from conception to completion.
• Passion for scientific discovery and innovation in AI.

• Conduct cutting-edge research in artificial intelligence and machine learning.
• Design, develop, and implement novel machine learning algorithms and models.
• Analyze and interpret complex datasets to extract actionable insights and build predictive models.
• Experiment with and evaluate various AI techniques, including deep learning, reinforcement learning, and generative models.
• Collaborate with other researchers and engineers to translate research findings into functional prototypes and product features.
• Publish research results in leading AI conferences and journals.
• Stay current with the latest advancements and trends in the field of AI and machine learning.
• Mentor and guide junior researchers and interns.
• Contribute to the strategic direction of the AI research roadmap.
• Present research findings to technical and non-technical audiences.

• Ph.D. or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a closely related quantitative field.
• 5+ years of post-graduate research experience in AI/ML.
• Demonstrated expertise in developing and implementing advanced machine learning models (e.g., deep neural networks, CNNs, RNNs, Transformers, GNNs).
• Proficiency in programming languages such as Python, and familiarity with AI/ML frameworks (e.g., TensorFlow, PyTorch, Keras).
• Strong mathematical and statistical background.
• Experience working with large-scale datasets and distributed computing environments is a plus.
• Excellent analytical, problem-solving, and critical-thinking skills.
• Strong written and verbal communication skills, with a proven ability to publish research.
• Ability to work independently and collaboratively in a research-oriented team.

• Lead and conduct advanced research in artificial intelligence and machine learning, focusing on areas such as deep learning, natural language processing, computer vision, or reinforcement learning.
• Develop and implement novel algorithms and models to solve complex problems.
• Design and execute research experiments, analyse results, and document findings.
• Collaborate with a team of researchers and engineers to translate research into practical applications.
• Publish research findings in leading academic conferences and journals.
• Stay abreast of the latest advancements and trends in AI and machine learning globally.
• Mentor and guide junior researchers and data scientists.
• Contribute to the strategic direction of the AI research roadmap.
• Develop and maintain strong collaborations with academic institutions and industry partners.
• Present research findings to technical and non-technical audiences.
• Ensure research methodologies are rigorous and reproducible.
• Identify opportunities for intellectual property development and patent applications.

• PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
• Minimum of 6 years of post-doctoral research experience or equivalent industry experience in AI/ML.
• Demonstrated track record of significant contributions to AI/ML research, evidenced by publications, patents, or open-source contributions.
• Expertise in one or more core areas of AI/ML (e.g., deep learning frameworks like TensorFlow, PyTorch; NLP; computer vision).
• Strong programming skills in Python and experience with relevant libraries (e.g., NumPy, SciPy, Scikit-learn).
• Experience with large-scale data processing and distributed computing.
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong leadership, communication, and collaboration abilities.
• Ability to work independently and lead research initiatives in a remote setting.
• Experience in cloud computing platforms (AWS, Azure, GCP) is beneficial.

Humanoid is the first AI and robotics company in the UK, creating the world’s most advanced, reliable, commercially scalable, and safe humanoid robots. Our first humanoid robot HMND 01 is a next-gen labour automation unit, providing highly efficient services across various use cases, starting with industrial applications.

Our Mission

At Humanoid we strive to create the world’s leading, commercially scalable, safe, and advanced humanoid robots that seamlessly integrate into daily life and amplify human capacity.

Vision

In a world where artificial intelligence opens up new horizons, our faith in its potential unveils a new outlook where, together, humans and machines build a new future filled with knowledge, inspiration, and incredible discoveries. The development of a functional humanoid robot underpins an era of abundance and well-being where poverty will disappear, and people will be able to choose what they want to do. We believe that providing a universal basic income will eventually be a true evolution of our civilization.

Solution

As the demands on our built environment rise, labour shortages loom. With the world’s workforce increasingly moving away from undesirable tasks, the manufacturing, construction, and logistics industries critical to our daily lives are left exposed. By deploying our general-purpose humanoid robots in environments deemed hazardous or monotonous, we envision a future where human well-being is safeguarded while closing the gaps in critical global labour needs.

Responsibilities:

• Train policies via representation learning, behaviour cloning and RL; own the full loop from data to deployment.
• Partner with teleoperations to drive data collection: specify what “good” looks like, ensure diversity/coverage, and close the gap between sim and real.
• Run pre-, mid- & post-training on multimodal LLM/VLM/VLA stacks; plug in new modalities (vision, audio, proprioception, LiDAR/point clouds, …) without breaking existing ones.
• Build and maintain continuous pipelines: ingest simulation + tele‑op logs, version them, apply weak‑supervision labelling, curate balanced datasets, and auto‑surface fresh failure cases into retraining.
• Work with MLOps & Data Platform teams to scale distributed training and optimize models for real‑time edge inference.

Requirements:

• 3+ years building deep‑learning systems (industry or research) with shipped models or published artifacts to show for it.
• Hands‑on with at least one of: LLMs, VLMs, or image/video generative models — architecture, training, and inference.
• Experience with deep learning infrastructure: streaming datasets, checkpointing & state management, distributed training strategies.
• Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code.
• You document experiments clearly and communicate trade‑offs crisply.

Nice-to-Have:

• Robotics or autonomous driving experience.
• RL for LLMs or robotics (PPO, DPO, SAC, etc.).
• Proven productization of deep nets (latency/throughput constraints, telemetry, on‑device optimization).
• Publications at ICLR/ICML/NeurIPS or equivalent open‑source contributions.
• Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open VLA frameworks.

What We Offer:

• Competitive salary plus participation in our Stock Option Plan
• Paid vacation and travel opportunities to our London, Vancouver, and Boston offices
• Office perks: free breakfasts, lunches, snacks, and regular team events
• Freedom to influence the product and own key initiatives
• Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics
• Startup culture prioritising speed, transparency, and minimal bureaucracy

Humanoid is the first AI and robotics company in the UK, creating the world’s most advanced, reliable, commercially scalable, and safe humanoid robots. Our first humanoid robot HMND 01 is a next-gen labour automation unit, providing highly efficient services across various use cases, starting with industrial applications.

Our Mission

At Humanoid we strive to create the world’s leading, commercially scalable, safe, and advanced humanoid robots that seamlessly integrate into daily life and amplify human capacity.

Vision

In a world where artificial intelligence opens up new horizons, our faith in its potential unveils a new outlook where, together, humans and machines build a new future filled with knowledge, inspiration, and incredible discoveries. The development of a functional humanoid robot underpins an era of abundance and well-being where poverty will disappear, and people will be able to choose what they want to do. We believe that providing a universal basic income will eventually be a true evolution of our civilization.

Solution

As the demands on our built environment rise, labour shortages loom. With the world’s workforce increasingly moving away from undesirable tasks, the manufacturing, construction, and logistics industries critical to our daily lives are left exposed. By deploying our general-purpose humanoid robots in environments deemed hazardous or monotonous, we envision a future where human well-being is safeguarded while closing the gaps in critical global labour needs.

Responsibilities:

• Train policies via representation learning, behaviour cloning and RL; own the full loop from data to deployment.
• Partner with teleoperations to drive data collection: specify what “good” looks like, ensure diversity/coverage, and close the gap between sim and real.
• Run pre-, mid- & post-training on multimodal LLM/VLM/VLA stacks; plug in new modalities (vision, audio, proprioception, LiDAR/point clouds, …) without breaking existing ones.
• Build and maintain continuous pipelines: ingest simulation + tele‑op logs, version them, apply weak‑supervision labelling, curate balanced datasets, and auto‑surface fresh failure cases into retraining.
• Work with MLOps & Data Platform teams to scale distributed training and optimize models for real‑time edge inference.

Requirements:

• 3+ years building deep‑learning systems (industry or research) with shipped models or published artifacts to show for it.
• Hands‑on with at least one of: LLMs, VLMs, or image/video generative models — architecture, training, and inference.
• Experience with deep learning infrastructure: streaming datasets, checkpointing & state management, distributed training strategies.
• Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code.
• You document experiments clearly and communicate trade‑offs crisply.

Nice-to-Have:

• Robotics or autonomous driving experience.
• RL for LLMs or robotics (PPO, DPO, SAC, etc.).
• Proven productization of deep nets (latency/throughput constraints, telemetry, on‑device optimization).
• Publications at ICLR/ICML/NeurIPS or equivalent open‑source contributions.
• Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open VLA frameworks.

What We Offer:

• Competitive salary plus participation in our Stock Option Plan
• Paid vacation and travel opportunities to our London, Vancouver, and Boston offices
• Office perks: free breakfasts, lunches, snacks, and regular team events
• Freedom to influence the product and own key initiatives
• Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics
• Startup culture prioritising speed, transparency, and minimal bureaucracy

Deep Learning Libraries Engineer

£80,000 - £100,000 & stock options

I am partnered with a cutting-edge hardware scale up, focused on RISC-V computing platforms, aiming to transform the industry. They are looking for a Deep Learning Libraries Engineer to Design and implement critical parts of the DL libraries, including kernels used by PyTorch, using C++. You'll also be contributing to the performance analysis flow for optimisation and to the functional and performance ISA simulators.

They have recently achieved significant Series A funding, therefore looking to considerably expand their Cambridge team. They are looking for someone that's passionate about high-performance systems and being able to to have an influence on the architectural decisions of the hardware engine.

What's required for this Deep Learning Libraries Engineer position?

• Strong C++ development skills
• Knowledge of parallel programming languages - CUDA, OpenCL, MPI, OpenMP etc
• Experience developing numerical Libraries
• Strong background in dense linear algebra software

If you are a Deep Learning Libraries Engineer looking for an exciting opportunity within a well-funded, growing scale-up, please apply to learn more!

If you are interested in this, or other software opportunities across the UK, please contact Jack Bird.

• Conduct cutting-edge research in deep learning, exploring new architectures, algorithms, and training methodologies.
• Develop, implement, and rigorously test advanced deep learning models for complex problem domains.
• Collaborate with a global team of researchers and engineers to translate research ideas into practical applications.
• Publish research findings in top-tier AI conferences and journals.
• Contribute to the intellectual property portfolio through patent applications.
• Stay abreast of the latest advancements and trends in the fields of AI, machine learning, and deep learning.
• Design and execute experiments to validate hypotheses and measure the performance of AI models.
• Develop robust and scalable AI solutions that can be integrated into production systems.
• Mentor junior researchers and interns, fostering a culture of innovation and knowledge sharing.
• Communicate complex technical concepts effectively to both technical and non-technical audiences.
• Contribute to the strategic direction of the research roadmap and identify new research opportunities.

• PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
• A minimum of 5 years of post-doctoral or industry research experience in deep learning.
• Proven track record of impactful research contributions, demonstrated through publications in leading AI venues (e.g., NeurIPS, ICML, ICLR, CVPR).
• Deep expertise in various deep learning frameworks such as TensorFlow, PyTorch, or JAX.
• Strong theoretical understanding of machine learning fundamentals, statistical modeling, and optimization techniques.
• Proficiency in programming languages commonly used in AI research, such as Python.
• Experience with large-scale data processing and distributed computing is highly desirable.
• Excellent problem-solving skills, creativity, and a passion for scientific discovery.
• Strong written and verbal communication skills, with the ability to articulate complex ideas clearly.
• Ability to work independently and collaboratively in a remote research environment.
• Experience in areas such as natural language processing, computer vision, reinforcement learning, or generative models is a plus.