Role Description:

We are looking for an independent, dedicated Senior ML Engineer to join the Visual Computing Group (VCG) and contribute to redefining the world of game play and live streaming via advanced R&D. VCG’s mission is to design and deploy advanced neural networks and machine learning (ML) for game rendering and streaming systems of SIE that exceed the state-of-the-art in runtime efficiency, visual quality and latency.

What you’ll be doing:

• Develop and improve core generative models for graphics and video processing for a variety of applications, such as live video streaming, game rendering, etc.
• Research and prototyping of new methods for graphics or video enhancement, and data generation pipelines from generative models
• Collaborate with team members on drafting publications and patent submissions
• Participate and assist the team in core R&D work with other teams within Sony Interactive Entertainment

What we’re looking for:

• PhD or MSc in Computer Science, Electronic Engineering, Artificial Intelligence, Machine Learning, Computer Graphics or a related field, or equivalent skills evidenced by work experience in the specific domain of the post
• 5+ years of experience in demonstrable research experience in industry or academia.
• Strong background in: TensorFlow or PyTorch; Python and packages/libraries related to computer vision or graphics; evidenced by the development of advanced applications in image/video/graphics processing or computer vision (minimum of 3 years of experience for ML engineer, minimum 5 years for Senior ML engineer),
• Evidence of leading technical developments by at least 1 research publication in a top-tier conference like CVPR, ECCV, ICCV, SIGGRAPH or similar
• Solid background in one or more of the following:

(i) neural network architectures, evidenced (for example) by knowledge of how to formulate and test advanced loss functions in neural network design;

(ii) design and test of advanced convolutional, recurrent, transformer-based, diffusion-based or other neural network architectures in a task-specific manner;

(iii) some experience in training, validation and evaluation of deep neural network models on large datasets, evidenced by experience in using Python libraries like HDF5 or similar

Desired qualifications:

• Publications, e.g. in top-tier conferences and journals: IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, conferences like IEEE CVPR/ICCV/ECCV, NeurIPS, ICML, ICLR, or similar
• Some theoretical understanding of the graphics pipeline, including coordinate transformations between frames of reference, PBR materials, rasterization, lighting, and ray tracing
• Experience in image processing theory and methods, evidenced by the development of practical designs in this area
• Some experience with 3D engines such as Unreal or Unity; in particular, generation of datasets, extraction of G-buffers, motion vectors, etc.

Benefits:

• Discretionary bonus opportunity
• Hybrid Working (within Flexmodes)
• Private Medical Insurance
• Dental Scheme
• 25 days holiday per year
• On Site Gym
• Subsidised Café
• Free soft drinks
• On site bar
• Access to cycle garage and showers

Experienced Deep Learning Engineer for Game Technology

We are looking for a machine learning engineer with a solid understanding of low-level systems programming. If you are as comfortable in the memory view of your debugger as you are with training networks to solve problems, we would love to hear from you.

This role could suit an experienced embedded/games industry programmer with a passion for Machine Learning, or an accomplished Machine Learning/Vision programmer with a good understanding of computer architecture and performance.

About the Advanced Technology Group

The Advanced Technology Group (ATG) is a part of Foundational Systems and Experiences Engineering (FSEE) within the Platform Business Group. Established over 20 years ago as part of PlayStation’s first party game development studios, ATG's focus has always been to help game developers to get the most out of PlayStation hardware, and this is still true today. The majority of the group holds responsibility for a large part of the GPU software stack used by all PlayStation games, which allows us to collaborate with engineering teams worldwide, from third party game developers to hardware vendors, to deliver key platform technologies.

You will be part of a small creative team within ATG, tackling problems centred around computer vision and machine learning. Examples of projects include a novel facial performance capture system used to produce assets for AAA games, and building a general purpose solution for on-console inference.

The Role

You will be expected to develop innovative ML solutions for deployment in games and in the development pipeline. This also involves making technology choices, prototype solutions and bringing them to production.

Some of these systems are expected to run on millions of PlayStations in people’s homes, making high run-time performance crucial. You should stay up to date with the competition and the industry’s latest advances and academic developments. The role also involves close collaboration with our other teams in Europe, the U.S and Japan, as well as technical communication of the ideas and solutions developed.

The Essential Requirements

• Experience with modern Deep Learning and Pytorch: You will be expected to make decisions about data and architectures, to implement pipelines for training and validation and to develop and deploy models in production.

• Experience with production-facing software development and strong debugging skills. This also requires demonstrating applied engineering skills through a shipped ML network or personal project.

• Understanding of 3D maths/geometry: Including multiple-view geometry and 3D mesh representations.

Nice-to-have

• Experience with Graphics and/or Game Engines

• Experience with Computer Vision

Benefits

• Discretionary bonus opportunity
• Private Medical Insurance
• Dental Scheme
• 25 days holiday per year
• On Site Gym
• Subsidised Café
• Free soft drinks
• On site bar
• Access to cycle garage and showers

Role Description:

We are looking for an independent, dedicated Machine Learning Engineer/Senior ML Engineer to join the Visual Computing Group (VCG) and contribute to redefining the world of game play and live streaming via advanced R&D. VCG’s mission is to design and deploy advanced neural networks and machine learning (ML) for game rendering and streaming systems of SIE that exceed the state-of-the-art in runtime efficiency, visual quality and latency.

What you’ll be doing:

• Develop and improve core generative models for graphics and video processing for a variety of applications, such as live video streaming, game rendering, etc.
• Research and prototyping of new methods for graphics or video enhancement, and data generation pipelines from generative models
• Collaborate with team members on drafting publications and patent submissions
• Participate and assist the team in core R&D work with other teams within Sony Interactive Entertainment

What we’re looking for:

• PhD or MSc in Computer Science, Electronic Engineering, Artificial Intelligence, Machine Learning, Computer Graphics or a related field, or equivalent skills evidenced by work experience in the specific domain of the post
• 3+ years of experience in demonstrable research experience in industry or academia.
• Strong background in: TensorFlow or PyTorch; Python and packages/libraries related to computer vision or graphics; evidenced by the development of advanced applications in image/video/graphics processing or computer vision (minimum of 3 years of experience for ML engineer, minimum 5 years for Senior ML engineer),
• Evidence of leading technical developments by at least 1 research publication in a top-tier conference like CVPR, ECCV, ICCV, SIGGRAPH or similar
• Solid background in one or more of the following:

(i) neural network architectures, evidenced (for example) by knowledge of how to formulate and test advanced loss functions in neural network design;

(ii) design and test of advanced convolutional, recurrent, transformer-based, diffusion-based or other neural network architectures in a task-specific manner;

(iii) some experience in training, validation and evaluation of deep neural network models on large datasets, evidenced by experience in using Python libraries like HDF5 or similar

Desired qualifications:

• Publications, e.g. in top-tier conferences and journals: IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, conferences like IEEE CVPR/ICCV/ECCV, NeurIPS, ICML, ICLR, or similar
• Some theoretical understanding of the graphics pipeline, including coordinate transformations between frames of reference, PBR materials, rasterization, lighting, and ray tracing
• Experience in image processing theory and methods, evidenced by the development of practical designs in this area
• Some experience with 3D engines such as Unreal or Unity; in particular, generation of datasets, extraction of G-buffers, motion vectors, etc.

Benefits:

• Discretionary bonus opportunity
• Hybrid Working (within Flexmodes)
• Private Medical Insurance
• Dental Scheme
• 25 days holiday per year
• On Site Gym
• Subsidised Café
• Free soft drinks
• On site bar
• Access to cycle garage and showers

At PlayStation, Data Science plays a critical role in shaping how we invest in, retain, and delight our global player base. The CLV & Personalisation team focuses on understanding player behaviour and driving more effective engagement across the player lifecycle — from acquisition and onboarding through to retention, monetisation, and long-term value. 

As a Senior Data Scientist, you will drive the development of advanced modelling and analytical approaches that influence key commercial and product decisions at scale and are evaluated based on their impact on commercial outcomes. You will work on complex and often ambiguous problems, helping define how we measure player value and how we deliver more personalised player experiences. 

This role combines deep hands-on modelling with ownership of problem definition, approach, and impact. You will work with a high degree of autonomy across ambiguous problem spaces, shaping how data informs strategy and decision-making across the business. 

What You’ll Do 

• Design and deliver advanced machine learning models to drive player value, engagement, and growth across the lifecycle. 

• Own ambiguous problem spaces, defining modelling approaches and breaking down complex business challenges into actionable solutions. 

• Partner with senior stakeholders to shape strategies, and ensure Data Science drives high-impact decisions. 

• Apply advanced machine learning techniques, including deep learning and sequence modelling, where appropriate to capture complex player behaviours and improve model performance. 

• Evaluate and select modelling approaches based on problem context, balancing performance, scalability, and interpretability. 

• Work within cross-functional teams, including engineering, product, and commercial stakeholders, to ensure solutions are scalable, production-ready, and aligned with long-term architecture. 

• Mentor other data scientists and contribute to best practices across the team. 

• Communicate complex modelling approaches, assumptions, and outcomes clearly to senior technical and non-technical audiences. 

What We’re Looking For 

You combine technical depth in machine learning with a proactive, solution-oriented mindset, and the ability to influence strategy and inspire others. You thrive on solving sophisticated problems that deepen PlayStation’s understanding of its players and you use this understanding to inform data-driven decisions and strategies that drive player growth. 

• Proven industry experience delivering impactful, production-ready machine learning solutions in areas such as customer value, retention, or personalisation. 

• Strong expertise in modern machine learning approaches, including applying techniques such as transformers, embeddings, or sequence-based models to real-world behavioural data. 

• Ability to critically assess when advanced approaches are warranted versus simpler solutions. 

• Experience owning projects end-to-end, including problem definition, navigating ambiguity, selecting modelling approaches, and delivering impactful solutions. 

• Ability to work with large, complex datasets and translate them into scalable solutions. 

• Strong stakeholder management and communication skills, with the ability to influence decisions and communicate results persuasively to both technical and non-technical audiences. 

• Proficiency in Python, SQL, and modern ML tooling (such as Tensorflow, PyTorch, MLlib, feature stores); experience with PySpark or equivalent distributed data processing tools. 

• A strong academic background, typically a Master’s or PhD in a technical or quantitative field, specialising in Machine Learning. 

• Experience deploying models into production and maintaining business-critical or consumer facing models. 

Nice to Have 

• Background in gaming, e-commerce, or subscription-based businesses. 

• Industry experience with experimentation frameworks and causal inference. 

• Experience working in agile, fast-paced environments 

Scale’s mission is to develop reliable AI systems for the world's most important decisions. Our core work consists of:

• Creating custom AI applications that will impact millions of citizens
• Generating high-quality training data for national LLMs
• Upskilling and advisory services to spread the impact of AI

Scale is hiring ML Research Engineers to bridge the gap between frontier research and real-world impact. While we solve critical challenges for global governments, your role will extend beyond implementation. You will lead the charge in research into Agent design, Deep Research and AI Safety/reliability, developing novel methodologies that not only power public sector applications but set new standards across the entire Scale organisation.

Your mission is threefold:

• Frontier Research & Publication: Leading research into LLM/agent capabilities, reasoning, and safety, with the goal of publishing at top-tier venues (NeurIPS, ICML, ICLR).
• Cross-Org Impact: Developing generalised techniques in Agent design, AI Safety and Deep Research agents that scale across our commercial and government platforms.
• Mission-Critical Applications: Engineering high-stakes AI systems that impact millions of citizens globally.

You will:

• Pioneer Novel Architectures: Design and train state-of-the-art models and agents, moving beyond “off-the-shelf” solutions to create custom architectures for complex public sector reasoning tasks.
• Lead AI Safety Initiatives: Research and implement robust safety frameworks, including red teaming, alignment (RLHF/DPO), and bias mitigation strategies essential for sovereign AI.
• Drive Deep Research Capabilities: Develop agents capable of long-horizon reasoning and autonomous information synthesis to solve complex problems for national security and public policy.
• Publish and Contribute: Represent Scale in the broader research community by publishing high-impact papers and contributing to open-source breakthroughs.
• Consult as a Subject Matter Expert: Act as a technical authority for public sector leaders, advising on the theoretical limits and safety requirements of emerging AI.
• Build Evaluation Frontiers: Create new benchmarks and evaluation protocols that define what success looks like for high-stakes, non-commercial AI applications.

Ideally, you’d have:

• Advanced Degree: PhD or Master’s in Computer Science, Mathematics, or a related field with a focus on Deep Learning.
• Research Track Record: A portfolio of first-author publications at major conferences (NeurIPS, ICML, CVPR, EMNLP, etc.).
• Engineering Rigour: Strong proficiency in Python, deep learning frameworks (PyTorch/JAX), with the ability to write production-ready code that scales.
• Safety Expertise: Experience in alignment, robustness, or interpretability research.

Nice to haves:

• Experience with large-scale distributed training on massive clusters.
• Experience in building agentic systems that are reliable.
• Experience in Sovereign AI or working with highly regulated data environments.
• A zero-to-one mindset: Comfortable navigating ambiguity and defining research directions from scratch.

Join our Talent Community!

We’re always looking to connect with exceptional talent. If what we’re building resonates with you, we’d love to learn more about your background and what you’re looking to do next.

If you’d like to be considered for future opportunities with us, please submit your information below. This is an expression of interest, so while we may not be in touch immediately, your information will be kept on file and reviewed as new roles open up. In the meantime, we encourage you to follow us on LinkedIn and check our careers page for updates.

We are hiring across our U.S. and London hubs, and occasionally have remote opportunities as well. If you see a role that aligns with your experience, we recommend applying directly to that posting so our team can review your application as soon as possible.

The role

Token Factory is a part of Nebius Cloud, one of the world's largest GPU clouds, running tens of thousands of GPUs. We are building a high-performance inference and fine-tuning platform designed to push foundation models to their hardware limits. Our mission is to maximize throughput, minimise latency, and optimise cost-per-token across tens of thousands of GPUs.

Some directions we are currently working on, and which you can be a part of:

• Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. Squeezing the maximum performance for a wide range of LLM architectures at scale (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-5).
• Inference engines support: Implement novel speculative decoding architectures, optimise components of various LLM designs (dense/MoE, autoregressive/parallel), and contribute to open-source inference engines.
• Low Precision Training & Inference: Design and productionise low-precision (FP8, NVFP4/MXFP4) training and inference pipelines with measurable gains in throughput and cost-efficiency.

We expect you to have:

• A profound understanding of theoretical foundations of machine learning and transformer architecture.
• Experience profiling GPU workloads using Nsight, PyTorch profiler, or similar tools
• Understanding of GPU memory hierarchy and compute/memory tradeoffs
• Familiarity with important ideas in LLM space, such as MHA, RoPE, KV-cache, Flash Attention, and quantisation
•  Understanding of performance aspects of large neural network training (sharding strategies, custom kernels, hardware features etc.)
•  Strong software engineering skills (we mostly use Python)
• Deep experience with modern deep learning frameworks
• Proficiency in contemporary software engineering approaches, including CI/CD, version control and unit testing
• Strong communication and leadership abilities

Nice to have:

• Experience working with open-source inference engines (vLLM, SGLang, TensorRT-LLM), including contributions
• Experience with kernel languages or DSLs such as Triton, Cute, CUTLASS, CUDA
• A track record of building and delivering products (not necessarily ML-related) in a dynamic startup-like environment.
• Strong engineering skills, including experience in developing large distributed systems or high-load web services.
• Open-source projects that showcase your engineering prowess
•  Excellent command of the English language, alongside superior writing, articulation, and communication skills.

Research Engineering (Machine Learning), London 

We are looking for Research Engineers with different levels of experience - Mid through to Senior, Staff, Principal or equivalent levels.

Your impact 

This is an exciting opportunity for you to contribute to frontier research at the intersection of AI and drug design.

Working in a highly creative, iterative environment, you will be partnering with scientists and engineers to advance foundational models that will transform the biopharmaceutical world as we know it.  

You will draw upon your existing engineering and Machine Learning experience whilst learning from those around you, to apply novel techniques and ideas to newly encountered computational biology and chemistry problems.

What you will do 

Implementation & Optimisation:

• Translate research concepts into practical implementations by developing and optimising state-of-the-art AI models, and building and maintaining robust codebases, data pipelines, and infrastructure for training and evaluation.

Experimentation & Evaluation:

• Design, implement, and run experiments to evaluate the performance and robustness of ML models, using a full spectrum of state-of-the-art machine learning methods. Evaluating, tuning, and maintaining AI/ML models (which includes collecting and preparing data as needed)

Evaluation & Inference:

• Implement algorithms and software to analyse and evaluate the performance of AI models.
• Optimising performance of AI/ML models such as Diffusion models, Transformers, GNNs, leveraging a deep understanding of the AI/ML hardware+software stack
• Advise on how to bring AI/ML models to production and/or integrating them into product offerings, and monitoring and refining their behavior.
• Developing specialised tools/frameworks/infrastructure to aid in the work above

Collaboration & Knowledge Sharing:

• Work closely with research scientists and engineers, contributing to team discussions, sharing knowledge, and actively participating in code reviews to foster a collaborative environment.

Innovation & Impact:

• Proactively identify and address technical challenges, stay updated on the latest AI advancements, and focus on developing solutions that enable scaling our wider foundation and applied model platforms.
• Ability to execute on independent engineering projects and software development towards research goals.

Skills and Qualifications

Essential

• PhD in technical subject with major engineering component and exposure to AI/ML, or BSc, MSc and 2+ years of specific experience working on ML model development:
• Strong general engineering experience, as evidenced by exposure to one or more of:
• Software design / algorithms, especially for deep learning frameworks
• Modern ML frameworks such as JAX, PyTorch or TensorFlow
• Distributed systems and runtimes
• Compilers (e.g. XLA, Triton, CUDA, Pallas, …)
• Large scale model training and serving infrastructure
• Experience in navigating complex research codebases
• Databases and data processing pipelines
• Numerical methods, simulation, optimisation

• Strong fundamentals in mathematics, statistics, linear algebra
• Experience with the full ML research and development lifecycle.
• Strong understanding of ML theory and applications.
• Strong understanding of data structures and algorithms.

Nice to have

• Interest in chemistry and biology.
• Experience working with biomedical data.
• Knowledge of the pharmaceutical industry, ideally with a focus on drug discovery.

What We're Looking For

We’re looking for a Fullstack Engineer to help develop and scale the Lightning AI platform across the frontend, CLI, APIs, and core backend systems. In this role, you’ll take ownership of key features and drive them end-to-end, working closely with engineering, product, and design to ship high-quality, iterative improvements. This is an opportunity to build systems that support real-world machine learning workloads at scale.

This role is based in one of our hubs (NYC, SF, Seattle, or London), with a minimum of 2 in-office days per week and occasional team and company offsites. We are not able to provide visa sponsorship for this position at this time.

What You’ll Do

• Write readable/testable/efficient code in React and either Python or Go (Golang).
• Build deep expertise in our technology stack to deliver new features, improve system stability, and increase overall performance.
• Partner with engineering and product leaders on developing the Lightning product while using your experience to set the technical direction for large projects.
• Evaluate, strengthen, and document technical architecture, tools, and processes.
• Maintain high standards for software quality, implement automation, drive continuous delivery, and reduce time to production while proactively reducing technical debt.
• Mentor and coach engineers on system design, operating in high uncertainty, and problem-solving to create a supportive, inclusive environment where each engineer can grow.

What You’ll Need

• Proficiency in React and either Go (Golang) or Python, with a preference for Go
• Strong understanding of software engineering principles and lifecycle
• Hands-on experience as a Fullstack Engineer in a SaaS technology company
• Proven ability to take ownership of key features and drive end-to-end development
• Communication and collaboration with engineers, product managers, and designers
• Ability to operate in high uncertainty and rapidly changing environments

Compensation

What We're Looking For

We’re looking for a Frontend Engineer to help develop and scale the UI and frontend infrastructure of the Lightning AI platform. In this role, you’ll take ownership of key features and drive them end-to-end, working closely with engineering, product, and design to ship high-quality, iterative improvements. With over 10,000 organizations building with Lightning, you’ll have a unique opportunity to impact how the world builds and deploys AI to production.

This role is based in one of our hubs (Seattle, NYC, SF, or London), with a minimum of 2 in-office days per week and occasional team and company offsites. We are not able to provide visa sponsorship for this position at this time.

What You’ll Do

• Write readable/testable/efficient code in React and Redux, and build deep expertise in our technology stack to deliver new features, improve system stability, and increase overall performance.
• Partner with engineering and product leaders on developing the user interface and frontend infrastructure while using your experience to set the technical direction for large projects.
• Evaluate, strengthen, and document technical architecture, tools, and processes.
• Maintain high standards for software quality, implement automation, drive continuous delivery, and reduce time to production while proactively reducing technical debt.
• Mentor and coach engineers on system design, operating in high uncertainty, and problem-solving to create a supportive, inclusive environment in which each engineer can grow.

What You’ll Need

• Proficiency in React, JavaScript and TypeScript.
• Strong understanding of software engineering principles and lifecycle.
• Hands-on experience as a Frontend Engineer in a SaaS technology company.
• Proven ability to take ownership of key features and drive end-to-end development.
• Communication and collaboration with engineers, product managers, and designers.
• Ability to operate in high uncertainty and rapidly changing environments.

Compensation

What We're Looking For

We’re looking for a Backend Engineer to help develop and scale the Lightning AI platform, spanning APIs, infrastructure, billing, security, and integrations. In this role, you’ll take ownership of key features and drive them end-to-end, working closely with engineering, product, and design to ship high-quality, iterative improvements. This is an opportunity to build systems that support real-world machine learning workloads at scale.

This role is based in one of our hubs (Seattle, NYC, SF, or London), with a minimum of 2 in-office days per week and occasional team and company offsites. We are not able to provide visa sponsorship for this position at this time.

What You’ll Do

• Write readable/testable/efficient code in Go (Golang).
• Build deep expertise in our technology stack to deliver new features, improve system stability, and increase overall performance.
• Partner with engineering and product leaders on developing the Lightning product while using your experience to set the technical direction for large projects.
• Evaluate, strengthen, and document technical architecture, tools, and processes.
• Maintain software quality, implement automation, drive continuous delivery, and reduce time to production while proactively reducing technical debt.
• Mentor and coach engineers on system design, operating in high uncertainty, and problem-solving to create a supportive, inclusive environment where each engineer can grow.

What You’ll Need

• Extensive experience writing efficient, maintainable code in Go. Familiarity with best practices in testing, readability, and optimization is essential.
• Broad knowledge of backend and frontend systems, with experience building and scaling APIs, CLI tools, billing systems, and security protocols. Exposure to frontend frameworks is a plus.
• Experience with cloud platforms like AWS, GCP, or Azure and designing systems that are reliable, performant, and scalable. Familiarity with containerization (Docker, Kubernetes) is a strong advantage.
• Proven ability to take ownership of key features and drive end-to-end development
• Commitment to software quality, test automation, and continuous delivery. Experience proactively managing technical debt and speeding up time to production.
• Hands-on experience as a backend or product engineer in a SaaS technology company
• Communication and collaboration with engineers, product managers, and designers
• Ability to operate in high uncertainty and rapidly changing environments

Compensation

About Graphcore

At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence.

Job Summary

As a researcher at Graphcore, you will contribute to the advancement of AI research, investigating new ideas that push the limits on important AI/ML problems. Specialised hardware has been the key driver of the progress of AI over the last decade, and we believe that hardware-aware AI algorithms and AI-aware hardware developments will continue to be critical to advancing this exciting field. As such, we’re looking for candidates who are keen scientists and engineers, with the theoretical and practical skills needed for impactful AI research.

We are looking for researchers with experience of AI in low-power, edge and embodied scenarios such as robotics, autonomous driving and augmented/virtual reality. We are interested in the training and deployment of multimodal AI models in these contexts, covering areas such as world models, real-time computer vision, generating and reasoning over audio/video streams.

The Team

Graphcore Research participates in both fundamental and applied research, to characterise the computational requirements of machine intelligence and to demonstrate how hardware can drive the next generation of innovative AI models. We publish at leading AI/ML conferences (NeurIPS, ICML, ICLR) as well as specialist workshops, and collaborate with other research teams and organisations across the world.

We pride ourselves on being a supportive and collaborative team, where we organise around our individual research interests to solve problems together in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications, including for sequence- and graph-based data. We’re based across London, Cambridge and Bristol, with projects and discussions that involve all our locations.

Perhaps the best way to get an idea of what we’re all about is to read one of our papers or an article on our blog. If you’re excited to work at the cutting edge of AI supported by new hardware and want to develop your skills in this area, we’d love to hear from you!

Responsibilities and Duties

• Generate AI/ML ideas, design experiments, implement them & evaluate results.
• Prepare, submit & present your work to AI conferences and workshops.
• Collaborate with researchers, silicon and software engineers at Graphcore to help define, build and test Graphcore’s next generation of AI hardware.

About you:

Essential:

• Master’s, PhD or equivalent experience in a technical discipline (e.g., Maths, Statistics, Computer Science, Physics, Chemistry, Biomedical Engineering).
• Experience in embodied AI, world models or robotics.
• Python programming in a modern deep learning framework, e.g. PyTorch or JAX.
• Familiar with deep learning fundamentals: models, optimisation, evaluation and scaling.
• Capable of designing, executing and reporting from ML experiments.
• Mathematics skills to support the above: calculus, probability theory and linear algebra.
• Experience submitting papers to international scientific conferences or workshops.

Desirable:

• Lower-level programming for hardware efficiency, e.g. C++/CUDA/Triton.
• Practical familiarity with hardware capabilities for deep learning – threads, caches, vector & matrix engines, data dependencies, bus widths and throttling.
• Practical familiarity with software stacks for deep learning – compilation, kernel fusion, XLA/ATen ops, streams, and asynchronous execution.
• Conference or workshop submissions in the fields of embodied AI, world models, or robotics.

Benefits

In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications

About Graphcore

At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence.

Job Summary

As a research engineer at Graphcore, you will contribute to the advancement of AI research, investigating new ideas that push the limits on important AI/ML problems. Specialised hardware has been the key driver of the progress of AI over the last decade, and we believe that hardware-aware AI algorithms and AI-aware hardware developments will continue to be critical to advancing this exciting field. We are therefore looking for individuals who combine strong machine learning experience with practical engineering skills to deliver impactful AI research. We are seeking AI researchers with strong software engineering experience, particularly in lower-level programming and performance optimisation for hardware efficiency. Our research spans a broad range of topics, including efficient training and inference, world models, life sciences, reinforcement learning, and beyond. You will work closely with researchers to generate ideas and translate them into scalable implementations, contributing to publications and projects that help to steer the future of AI hardware.

The Team

Graphcore Research participates in both fundamental and applied research, to characterise the computational requirements of machine intelligence and to demonstrate how hardware can drive the next generation of innovative AI models. We publish at leading AI/ML conferences (NeurIPS, ICML, ICLR) as well as specialist workshops, and collaborate with other research teams and organisations across the world.

We pride ourselves on being a supportive and collaborative team, where we organise around our individual research interests to solve problems together in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications, including for sequence- and graph-based data. We’re based across London, Cambridge and Bristol, with projects and discussions that involve all our locations.

Perhaps the best way to get an idea of what we’re all about is to read one of our papers or an article on our blog. If you’re excited to work at the cutting edge of AI supported by new hardware and want to develop your skills in this area, we’d love to hear from you!

Responsibilities and Duties

• Generate AI/ML ideas, design experiments, implement them & evaluate results.
• Prepare, submit & present your work to AI conferences and workshops.
• Provide technical insight to internal teams by designing experiments and delivering clear, actionable reports.
• Collaborate with researchers, silicon and software engineers at Graphcore to help define, build and test Graphcore’s next generation of AI hardware.

About you:

Essential:

•  Master’s, PhD or equivalent experience in a technical discipline (e.g., Maths, Statistics, Computer Science, Physics, Chemistry).
• Python programming in a modern deep learning framework, e.g. PyTorch or JAX.
• Familiar with deep learning fundamentals: models, optimisation, evaluation and scaling.
• Capable of designing, executing and reporting from ML experiments.
• Lower-level programming for hardware efficiency, e.g. C++/CUDA/Triton.
• Practical familiarity with hardware capabilities for deep learning – threads, caches, vector & matrix engines, data dependencies, bus widths and throttling.
• Practical familiarity with software stacks for deep learning – compilation, kernel fusion, XLA/ATen ops, streams, and asynchronous execution

Desirable:

• Mathematics skills to support the above: calculus, probability theory and linear algebra.
• Experience submitting papers to international scientific conferences or workshops.

Benefits

In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications

The deadline for applying to this role is Sunday 24th May 2026, end of day, anywhere on Earth.

Team Description    

The ability to effectively evaluate and monitor AI systems will grow in importance as models become more capable, autonomous, and integrated into society. If models can detect and game evaluations, obscure their reasoning, or behave differently under observation, the safety claims that governments and developers rely on become unreliable. Understanding and addressing these risks is essential to ensuring that oversight of advanced AI systems keeps pace with their capabilities. 

The Model Transparency team is a research team within AISI focused on ensuring that evaluations, assessments, and monitoring of frontier AI systems remain reliable as models become less transparent. We research how and why oversight is declining – through phenomena such as evaluation awareness, unfaithful chain-of-thought reasoning, and changes in model architectures – and develop methods (including white and black box methods) to detect, measure, and mitigate potential issues. We share our findings with frontier AI companies (including Anthropic, OpenAI, DeepMind), UK government officials, and allied governments, and publicly to inform their deployment, research, and policy decisions. We also work directly with safety teams at frontier labs, contributing to safety case reviews and helping improve their alignment evaluation methodology. 

Our recent work includes auditing games for sandbagging, reproducing natural emergent misalignment from reward hacking, and identifying open-weight language models that game propensity evaluations. 

Role description

We're looking for Research Scientists and Research Engineers for the Model Transparency team with expertise in technical AI safety – such as interpretability, capability or alignment evaluations, model transparency – or with broader experience with frontier LLM research and development. An ideal candidate would have a strong track record of high-quality research in technical AI safety or adjacent fields. 

• Research Scientists, drive the technical substance of our work – staying abreast of the literature, proposing and designing experiments, conducting rigorous analyses, and owning the evidence stack from experiment through to written output. They write, critique, and strengthen the team's reports and publications. 
• Research Engineers, build the systems and tooling that make our research possible and fast – scaling experimental workflows, automating processes, solving infrastructure challenges, and creating systems that accelerate the entire team's output.

We're interested in candidates along the spectrum between Research Engineers and Research Scientists. The application form will ask you to indicate which role you lean towards. 

The team is led by Joseph Bloom, advised by Geoffrey Irving. You'll work with talented, mission-driven technical staff across AISI, including alumni from Anthropic, OpenAI, DeepMind, and top universities. You may also collaborate with external research teams including those at frontier AI labs, METR, and FAR. 

We are open to hires across a range of experience levels.

This role requires three days a week in person, with flexibility for occasional periods of remote working. 

Representative Projects You Might Work On

• Developing a chain-of-thought monitorability benchmark and comparing monitorability properties across frontier AI systems, leveraging AISI’s unique access to reasoning traces from multiple labs. 

• Designing and running experiments on open-weight models to study alignment and oversight-relevant phenomena – such as reproducing emergent misalignment from reward hacking, or red-teaming techniques like inoculation prompting and character training. 

• Using white-box and interpretability methods – such as activation oracles, sparse auto-encoders or probes – to detect misalignment that isn’t visible through behavioural evaluation alone. 

• Building tooling and infrastructure for our research – including agent orchestration, large-scale RL pipelines, mechanistic interpretability methodologies, and auditing agents. 

The work could also involve: 

• Reviewing frontier lab risk assessments and safety cases, providing independent analysis of alignment claims before deployment decisions. 
• Conducting literature reviews and expert interviews to map the state of model transparency risks and inform AISI’s strategic priorities. 
• Translating technical findings into actionable insights for AISI evaluation teams, UK government officials, and international partners. 

What we’re looking for   

If you’re unsure whether you meet the criteria below, we’d encourage you to apply anyway – we’d rather you erred on the side of applying than not. 

Requirements for both roles:

• A get-things-done mindset – you take ownership, move fast, and care about shipping work that matters. 

• A combination of self-sufficiency and enthusiasm for teamwork – you’re equally happy defining your own agenda and contributing to shared goals. You’re excited about growing, giving and receiving feedback, and building something together. 

• An ability to build, supervise and orchestrate AI agents to complete tasks effectively, while verifying and maintaining quality of work. 

• A demonstrated track record of relevant, high-quality work – whether technical publications, blog posts, or other publicly visible contributions. 

Research Scientists – our requirements are: 

• Hands-on research experience with large language models (LLMs) – such as evaluating or fine-tuning models, developing and testing monitors, or auditing models with white-box or black-box techniques. 

• Ability and experience in writing research code for machine learning experiments, including experience with ML frameworks like PyTorch or evaluation frameworks like Inspect. 

• An ability to write high-quality, concise research proposals that are well-motivated, tractable, and coherent. 

• Good research taste – an ability to identify what’s important, choose productive directions, and avoid getting lost in dead ends. 

• An ability to read research critically, identify flawed arguments, and poke holes in safety claims. 

We don’t expect RS candidates to meet all of the following, but they are useful signal: 

• Experience designing and running alignment evaluations or working on model transparency research. 

• Experience with interpretability or white-box methods – such as mechanistic interpretability, sparse autoencoders, probing, or activation analysis. 

• Familiarity with alignment literature, current methods for post-training and aligning LLMs, and the current state of the field. 

• Prior mentorship or training within technical AI safety – such as through the MATS program or similar. 

Research Engineers – our requirements are: 

• Strong software engineering skills and experience building systems that support ML research – infrastructure, pipelines, tooling, or experimental platforms. 

• Ability and experience writing production-quality code in Python and familiarity with ML frameworks like PyTorch. 

• Experience working with LLMs at scale in some capacity – fine-tuning, deploying, evaluating, or building scaffolds around them. 

• An understanding of the needs of research scientists, experience working within and supporting a research team or building tools to support research. 

We don’t expect RE candidates to meet all of the following, but they are useful signal: 

• A track record of scaling AI automation – getting agents to do useful work, building orchestration systems, or accelerating research workflows with AI tooling. 

• Experience working with very large models (~100B+) at scale, including post-training (RL, RLHF, DPO), fine-tuning pipelines, or distributed interpretability work on models that don’t fit into memory. 

• Experience with mechanistic interpretability tooling or white-box analysis infrastructure at scale. 

• Strong open-source contributions, particularly related to LLMs or AI safety. 

• Proficient usage of LLM coding tools and agents. 

Selection process  

Candidates should expect to go through some or all of the following stages: 

1. CV / application review 
2. Screening call
3. Technical assessment
4. Research interview
5. Behavioural interview
6. Final interview with members of the senior leadership team

Senior: PLN 260,400 - 352,200
Staff: PLN 350,700 - 474,400
Subject to alignment to the responsibilities and duties of the role - we currently have multiple positions available at both Senior and Staff level

About Graphcore 

At Graphcore, we’re building the future of AI compute.

We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.

As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.

To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.

We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence

Job Summary

As a Senior Machine Learning Engineer in the Applied AI team at Graphcore, you will contribute to advancing AI technology by developing and optimising AI models tailored to our specialised hardware. You will work on large scale systems where performance is critical to the success of our projects. Working closely with the Software development and Research teams, you will play a critical role in identifying opportunities to innovate and differentiate Graphcore’s technology. We seek engineers with strong technical skills and an understanding of AI model implementation at scale, eager to make a tangible impact in this rapidly evolving field.

The Team

The Applied AI team’s role is to be proxies for our customers, we need to understand the latest AI models, applications, and software to ensure that Graphcore’s technology works seamlessly with the AI ecosystem and at scale. We build reference applications, contribute to key software libraries e.g. optimising kernels for efficiency on our hardware, and collaborate with the Research team to develop and publish novel ideas in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications.

If you're excited about advancing the next generation of AI models on cutting-edge hardware, we’d love to hear from you!

Responsibilities and Duties

• Implement latest machine learning models and optimise them for performance and accuracy, scaling to 1000s of accelerators.
• Test and evaluate new internal software releases, provide feedback to software engineering teams, make necessary code fixes, and conduct code reviews.
• Benchmark models and key ML techniques to identify performance bottlenecks and improve model efficiency.
• Design and conduct experiments on novel AI methods, implement them and evaluate results.
• Collaborate with Research, Software, and Product teams to define, build, and test Graphcore’s next generation of AI hardware.
• Engage with AI community and keep in touch with the latest developments in AI.

Candidate Profile

Essential:

• Bachelor/Master's/PhD or equivalent experience in Machine Learning, Computer Science, Maths, Data Science, or related field.
• Proficiency in deep learning frameworks like PyTorch/JAX.
• Strong Python or C++ software development skills
• Expertise in deep learning from model training to optimisation and evaluation.
• Experience in distributed training or inference of ML models across 64+ accelerators.
• Capable of designing, executing and reporting from ML experiments.
• Developed deep understanding of performance bottlenecks and how to overcome them.
• Ability to move quickly in a dynamic environment
• Enjoy cross-functional work collaborating with other teams.
• Strong communicator - able to explain complex technical concepts to different audiences.

Desirable:

• Experience in one or more of:
  • MLOps for Kubernetes-based clusters
  • Building production systems with large language models
  • Efficient computing based on low-precision arithmetic.
• Experience writing C++/Triton/CUDA kernels for performance optimisation of ML models.
• Familiarity with HPC systems and networking including Infiniband, NVLink, RoCE technologies.
• Have contributed to open-source projects or published research papers in relevant fields.
• Knowledge of cloud computing platforms.
• Keen to present, publish and deliver talks in the AI community.

Benefits

In addition to a competitive salary, Graphcore offers annual leave policy, medical and dental health plans, a gym card, and employee pension (matched up to 4%). We review our benefits on a yearly basis to ensure we offer a valuable and rewarding benefits programme to our employees. We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

About Graphcore

At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence.  

Job Summary

As a Senior Machine Learning Engineer in the Applied AI team at Graphcore, you will contribute to advancing AI technology by developing and optimising AI models tailored to our specialised hardware. You will work on large scale systems where performance is critical to the success of our projects. Working closely with the Software development and Research teams, you will play a critical role in identifying opportunities to innovate and differentiate Graphcore’s technology. We seek engineers with strong technical skills and an understanding of AI model implementation at scale, eager to make a tangible impact in this rapidly evolving field.

The Team

The Applied AI team’s role is to be proxies for our customers, we need to understand the latest AI models, applications, and software to ensure that Graphcore’s technology works seamlessly with the AI ecosystem and at scale. We build reference applications, contribute to key software libraries e.g. optimising kernels for efficiency on our hardware, and collaborate with the Research team to develop and publish novel ideas in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications.

If you're excited about advancing the next generation of AI models on cutting-edge hardware, we’d love to hear from you!

Responsibilities and Duties

• Implement latest machine learning models and optimise them for performance and accuracy, scaling to 1000s of accelerators.
• Test and evaluate new internal software releases, provide feedback to software engineering teams, make necessary code fixes, and conduct code reviews.
• Benchmark models and key ML techniques to identify performance bottlenecks and improve model efficiency.
• Design and conduct experiments on novel AI methods, implement them and evaluate results.
• Collaborate with Research, Software, and Product teams to define, build, and test Graphcore’s next generation of AI hardware.
• Engage with AI community and keep in touch with the latest developments in AI.

Candidate Profile

Essential:

• Bachelor/Master's/PhD or equivalent experience in Machine Learning, Computer Science, Maths, Data Science, or related field.
• Proficiency in deep learning frameworks like PyTorch/JAX.
• Strong Python or C++ software development skills
• Expertise in deep learning from model training to optimisation and evaluation.
• Experience in distributed training or inference of ML models across 64+ accelerators.
• Capable of designing, executing and reporting from ML experiments.
• Developed deep understanding of performance bottlenecks and how to overcome them.
• Ability to move quickly in a dynamic environment
• Enjoy cross-functional work collaborating with other teams.
• Strong communicator - able to explain complex technical concepts to different audiences.

Desirable:

• Experience in one or more of:
  • MLOps for Kubernetes-based clusters
  • Building production systems with large language models
  • Efficient computing based on low-precision arithmetic.
• Experience writing C++/Triton/CUDA kernels for performance optimisation of ML models.
• Familiarity with HPC systems and networking including Infiniband, NVLink, RoCE technologies.
• Have contributed to open-source projects or published research papers in relevant fields.
• Knowledge of cloud computing platforms.
• Keen to present, publish and deliver talks in the AI community.

Benefits

In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications

About Graphcore

At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence.

Job Summary

As a Senior Machine Learning Engineer in the Applied AI team at Graphcore, you will contribute to advancing AI technology by developing and optimising AI models tailored to our specialised hardware. You will work on large scale systems where performance is critical to the success of our projects. Working closely with the Software development and Research teams, you will play a critical role in identifying opportunities to innovate and differentiate Graphcore’s technology. We seek engineers with strong technical skills and an understanding of AI model implementation at scale, eager to make a tangible impact in this rapidly evolving field.

The Team

The Applied AI team’s role is to be proxies for our customers, we need to understand the latest AI models, applications, and software to ensure that Graphcore’s technology works seamlessly with the AI ecosystem and at scale. We build reference applications, contribute to key software libraries e.g. optimising kernels for efficiency on our hardware, and collaborate with the Research team to develop and publish novel ideas in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications.

If you're excited about advancing the next generation of AI models on cutting-edge hardware, we’d love to hear from you!

Responsibilities and Duties

• Implement latest machine learning models and optimise them for performance and accuracy, scaling to 1000s of accelerators.
• Test and evaluate new internal software releases, provide feedback to software engineering teams, make necessary code fixes, and conduct code reviews.
• Benchmark models and key ML techniques to identify performance bottlenecks and improve model efficiency.
• Design and conduct experiments on novel AI methods, implement them and evaluate results.
• Collaborate with Research, Software, and Product teams to define, build, and test Graphcore’s next generation of AI hardware.
• Engage with AI community and keep in touch with the latest developments in AI.

Candidate Profile

Essential:

• Bachelor/Master's/PhD or equivalent experience in Machine Learning, Computer Science, Maths, Data Science, or related field.
• Proficiency in deep learning frameworks like PyTorch/JAX.
• Strong Python or C++ software development skills
• Expertise in deep learning from model training to optimisation and evaluation.
• Experience in distributed training or inference of ML models across 64+ accelerators.
• Capable of designing, executing and reporting from ML experiments.
• Developed deep understanding of performance bottlenecks and how to overcome them.
• Ability to move quickly in a dynamic environment
• Enjoy cross-functional work collaborating with other teams.
• Strong communicator - able to explain complex technical concepts to different audiences.

Desirable:

• Experience in one or more of:
  • MLOps for Kubernetes-based clusters
  • Building production systems with large language models
  • Efficient computing based on low-precision arithmetic.
• Experience writing C++/Triton/CUDA kernels for performance optimisation of ML models.
• Familiarity with HPC systems and networking including Infiniband, NVLink, RoCE technologies.
• Have contributed to open-source projects or published research papers in relevant fields.
• Knowledge of cloud computing platforms.
• Keen to present, publish and deliver talks in the AI community.

Benefits

In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications

About Graphcore

At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence.

Job Summary

As a Senior Machine Learning Engineer in the Applied AI team at Graphcore, you will contribute to advancing AI technology by developing and optimising AI models tailored to our specialised hardware. You will work on large scale systems where performance is critical to the success of our projects. Working closely with the Software development and Research teams, you will play a critical role in identifying opportunities to innovate and differentiate Graphcore’s technology. We seek engineers with strong technical skills and an understanding of AI model implementation at scale, eager to make a tangible impact in this rapidly evolving field.

The Team

The Applied AI team’s role is to be proxies for our customers, we need to understand the latest AI models, applications, and software to ensure that Graphcore’s technology works seamlessly with the AI ecosystem and at scale. We build reference applications, contribute to key software libraries e.g. optimising kernels for efficiency on our hardware, and collaborate with the Research team to develop and publish novel ideas in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications.

If you're excited about advancing the next generation of AI models on cutting-edge hardware, we’d love to hear from you!

Responsibilities and Duties

• Implement latest machine learning models and optimise them for performance and accuracy, scaling to 1000s of accelerators.
• Test and evaluate new internal software releases, provide feedback to software engineering teams, make necessary code fixes, and conduct code reviews.
• Benchmark models and key ML techniques to identify performance bottlenecks and improve model efficiency.
• Design and conduct experiments on novel AI methods, implement them and evaluate results.
• Collaborate with Research, Software, and Product teams to define, build, and test Graphcore’s next generation of AI hardware.
• Engage with AI community and keep in touch with the latest developments in AI.

Candidate Profile

Essential:

• Bachelor/Master's/PhD or equivalent experience in Machine Learning, Computer Science, Maths, Data Science, or related field.
• Proficiency in deep learning frameworks like PyTorch/JAX.
• Strong Python or C++ software development skills
• Expertise in deep learning from model training to optimisation and evaluation.
• Experience in distributed training or inference of ML models across 64+ accelerators.
• Capable of designing, executing and reporting from ML experiments.
• Developed deep understanding of performance bottlenecks and how to overcome them.
• Ability to move quickly in a dynamic environment
• Enjoy cross-functional work collaborating with other teams.
• Strong communicator - able to explain complex technical concepts to different audiences.

Desirable:

• Experience in one or more of:
  • MLOps for Kubernetes-based clusters
  • Building production systems with large language models
  • Efficient computing based on low-precision arithmetic.
• Experience writing C++/Triton/CUDA kernels for performance optimisation of ML models.
• Familiarity with HPC systems and networking including Infiniband, NVLink, RoCE technologies.
• Have contributed to open-source projects or published research papers in relevant fields.
• Knowledge of cloud computing platforms.
• Keen to present, publish and deliver talks in the AI community.

Benefits

In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications

About us

At Graphcore, we’re building the future of AI compute.

We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.

As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.

We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence.

Job Summary

As a research intern at Graphcore, you will advance AI research and investigate new ideas that push the limits on important AI/ML problems. Specialised hardware has driven progress in AI over the last decade, and we believe that hardware-aware AI algorithms and AI-aware hardware developments will continue to be critical to this exciting field. We’re looking for candidates who are keen scientists and engineers, with the theoretical and practical skills needed for impactful AI research.

During your internship, you’ll investigate a research question that relates to machine learning or accelerated computation. Practically, this means designing experiments, implementing models and algorithms, exploring data and presenting results, with the aim to present work publicly to the wider research community. You will be largely self-directed in your research activity, with freedom to pursue the most promising approach, and with the benefit of appropriate mentoring and guidance.

Our team is interested in numerous topics across the field, including (but not limited to) efficient training and inference, improved model capabilities in the areas of factuality and reasoning, image and video generation, world models and molecule generation for life science applications.

The Team

Graphcore Research participates in both fundamental and applied research, to characterise the computational requirements of machine intelligence and to demonstrate how hardware can drive the next generation of innovative AI models. We publish at leading AI/ML conferences (NeurIPS, ICML, ICLR) as well as specialist workshops, and collaborate with other research teams and organisations across the world.

Candidate profile

We’re looking for technically-minded, highly-motivated and adaptable problem-solvers who can thrive in our collaborative team.

Essential

• A PhD student working in the field of Machine Learning and Artificial Intelligence, or be pursuing a Masters degree in a relevant field with demonstrable research experience (for example, interesting ML publications);
• Able to demonstrate your coding skills (Python, C++), for example through projects or previous work experience, and have experience of algorithm implementation in machine learning frameworks such as PyTorch;
• Passionate about the field of AI, both theory and practice, with a keen interest to continue learning.

We are excited to hear about your research! Please provide a short summary of a recent project with your application (and we’d love to hear about any other relevant work).

Benefits

In addition to a competitive salary, our centrally located offices provide a well-stocked kitchen with healthy food, drinks and snacks and our very own barista (Bristol only for now, but we have great coffee everywhere). We have an active social scene too: cycling, yoga, running, board games, table tennis, football, to name a few.

We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

Hudson River Trading (HRT) is a quantitative trading firm at the forefront of technological innovation. We build and deploy cutting-edge systems within one of the world’s most advanced computing environments to power our global trading operations. Our non-siloed, collaborative coding environment empowers talented engineers to make significant contributions and see their impact daily. At HRT, you'll be challenged to solve the most complex problems in trading alongside many of the brightest minds in the field.

We are seeking highly motivated and skilled Research Engineers who will work very closely with our Algo Developer (Quant Research) trading teams. These roles require exceptional programming fundamentals, a strong user focus, and the ability to thrive in a fast-paced environment with constantly evolving requirements. Multitasking and context switching are inherent aspects of this role, through which you will gain insight into the ideation and execution of some of the world's most sophisticated trading strategies.  

Research Engineers at HRT work on a wide variety of projects. Examples include:

• Designing highly customized quantitative systems that enable Algo Developers to easily express and backtest new predictive signals.
• Building world-class research tooling to speed up Algo Developers’ research iteration.
• Aiding the development and deployment of very large deep learning models for HRT’s live trading.
• Improving HRT’s monetization systems which turn our predictions into profitable trades.
• Evaluating sim vs. live differences to improve the fidelity of our simulations.
• Building systems to monitor and manage live trading.
• Specializing in specific time horizons, from high frequency trading to mid-to-low frequency hedge fund-style strategies.
• Working across a very wide variety of asset classes (from equities to fixed income to options) and regions (from North America to Asia Pacific) – you’ll interact with markets across the world!

Responsibilities

• Design, develop, and maintain robust, performant, and scalable software systems.
• Optimize existing codebases for performance, reliability, and efficiency.
• Debug complex issues across distributed systems in a time-sensitive environment.
• Optimize both the mechanics and ergonomics of Algo Developer research workflows; this requires developing a deep intuition for research methodologies.
• Balance the short term goals of individual trading teams with the longer term goals of HRT’s generalized trading platforms across trading teams. Because we don’t work in a pod environment, REs on different trading teams are expected to share technology and ideas.

Qualifications

• Completed a Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field. If you are currently pursuing your Bachelor's degree, please apply to our Campus/New Grad opportunities.
• Previous experience at a top-tier finance or technology company with an excellent track record of rapid advancement and superlative reviews.
• Experience with supporting internal users is preferred; bonus if you have real hands-on experience with quant research and/or ML/Deep Learning platforms.
• Experience with python numerical, ML and data-oriented libraries is a big plus (e.g. pandas, scikit, pytorch, etc.)
• Candidates should be prepared for technical interviews in either Python or C++. While there is no hard language requirement for RE roles, proficiency in one of these languages will be assessed during the interview process.
• Kindness and empathy is a real requirement. No “smart jerks” at HRT. 
• Strong understanding of data structures, algorithms, and design principles.
• Excellent communication skills, both written and verbal.
• HRT ships every day. The ability to thoughtfully, methodically, and reliably deploy new software and features is a critical skill. 
• If you have management experience, that’s always a bonus – but even so, expect to code!

The estimated base salary range for this position is 200,000 to 300,000 USD per year (or local equivalent). The base pay offered may vary depending on multiple individualized factors, including location, job-related knowledge, skills, and experience. This role will also be eligible for discretionary performance-based bonuses and a competitive benefits package.

For more info about interviewing at HRT, see this blog!

Hudson River Trading (HRT) is looking for a Senior Software Engineer to focus on our Treasury Infrastructure. Coders at HRT work on small, highly productive, and efficient teams that design, improve, and maintain the technology that powers worldwide trading — at HRT, the code you write is our business. You’ll have an opportunity to work closely with Treasury Quantitative Researchers and our Funding Team to design and develop HRT's Treasury Optimization and Research Platform.

We are looking for highly skilled programmers who love to code and solve complex problems, appreciate a culture of collaboration, and thrive in a performance-driven environment. You can always expect to be challenged by the ever-changing financial markets, and find yourself working on critical software in an extremely fast-paced, real-time environment.

Responsibilities

• Contribute to the engineering design and development of HRT’s Treasury Optimization Platform.
• Collaborate with Treasury Researchers on HRT’s Treasury Research Infrastructure.
• Collaborate with Quantitative Developers as well as our Funding and Finance teams on managing cash and collateral requirements.

Qualifications

• Completed a Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field. If you are currently pursuing your Bachelor's degree, please apply to our Campus/New Grad opportunities.
• Working experience in Python and good CS fundamentals.
• Proficiency with numerical computing libraries (Numpy, PyTorch, TensorFlow) is a plus.
• Knowledge of global portfolio financing and funding is a plus.
• Strong problem-solving skills and ability to work in a fast-paced environment.
• Excellent communication skills and ability to collaborate with engineers, researchers, and non-technical teams.

The estimated base salary range for this position is 200,000 to 300,000 USD per year (or local equivalent). The base pay offered may vary depending on multiple individualized factors, including location, job-related knowledge, skills, and experience. This role will also be eligible for discretionary performance-based bonuses and a competitive benefits package.

Hudson River Trading (HRT) is hiring an AI Researcher to join the HAIL team. HAIL (HRT AI Labs) is the team at HRT responsible for developing and maintaining our most powerful models, which are used by our trading teams to drive a significant fraction of our trading. We are building and deploying "foundation models for markets", that ingest and train on vast amounts of market data, to make predictions about future market state. We are seeking experienced AI researchers to join our team to accelerate our efforts.

Researchers have great independence to pursue the research directions they think would be most impactful as part of a small focused team with minimal bureaucracy. They are enabled by state-of-the-art research clusters with very high GPU-to-researcher ratios, and supported by excellent engineering, hardware, and systems teams to realize their vision. Your work will be directly, clearly, highly impactful on the business, and it will be challenging: this is a field with no easy or obvious solutions. You'll be responsible for improving every part of our models: from featurization of data, to architecture design, to training dynamics, to how trading decisions are made.

Qualifications

• Must have two or more years work experience in deep learning research, for any domain: robotics, biology, chemistry, physics, audio, video, etc.
• Experience translating methods between areas of application is highly valued.
• LLM experience is valuable, but not necessary.
• Strong engineering skills valued, especially any of: GPU kernel development, lower-level Pytorch/JAX development, intermediate C++, FPGA/ASIC experience, HPC experience.

The estimated base salary range for this position is 200,000 to 300,000 USD per year (or local equivalent). The base pay offered may vary depending on multiple individualized factors, including location, job-related knowledge, skills, and experience. 

This role will also be eligible for discretionary performance-based bonuses and a competitive benefits package which includes medical, dental, vision, basic life insurance, and enrollment in our company’s retirement savings plans. Employees will receive sick and parental leave, as well as other paid time off (including 20 vacation days and 10 paid holidays in the US). Please note that benefits and time off policies will vary across non-US locations.

Who we are

Helsing is a defence AI company. Our mission is to protect our democracies. We aim to achieve technological leadership, so that open societies can continue to make sovereign decisions and control their ethical standards.

As democracies, we believe we have a special responsibility to be thoughtful about the development and deployment of powerful technologies like AI. We take this responsibility seriously.

We are an ambitious and committed team of engineers, AI specialists and customer-facing programme managers. We are looking for mission-driven people to join our European teams – and apply their skills to solve the most complex and impactful problems. We embrace an open and transparent culture that welcomes healthy debates on the use of technology in defence, its benefits, and its ethical implications.

The role

At Helsing, we are pioneering the future of autonomous decision-making for defence. Our work spans the full AI landscape, including high-volume data processing, RL agent training, and large-scale foundation models. As a member of a cross-functional team, you will architect and implement the tools and platforms that enable these breakthroughs. Your focus will be on abstracting complex distributed systems to maximise training throughput and developer velocity. We are looking for engineers who can navigate the convergence of machine learning and systems engineering to build robust, scalable platforms.

What you will do:

• Extend our highly integrated deep learning frameworks (built on top of PyTorch), making them efficient and easy to use for a wide range of use cases.

• Scale our current infrastructure and tooling stack to support faster and larger distributed training.

• Design data strategy to support large scale datasets and efficient storage, ensuring GPUs stay warm.

You should apply if you:

• Hold an MSc or PhD in Computer Science or STEM field, with a focus on Machine Learning and Deep Learning.

• Have strong software engineering skills in Python and fluency with modern DL frameworks (PyTorch/JAX/TensorFlow). You don’t just import libraries; you are comfortable writing custom layers, loss functions, and distributed training loops.

• Are a clear communicator who can build from complex theoretical concepts and contribute to the company's internal engineering culture.

• Have a "first-principles" mindset: you enjoy reading the latest AI optimisation blog posts and integrating them into codebases rapidly.

• You have debugged production ML pipelines and can tell a good war story about finding a subtle numerical or performance issue.

Note: We operate at an intersection where women, as well as other minority groups, are systematically under-represented. We encourage you to apply even if you don’t meet all the listed qualifications; ability and impact cannot be summarised in a few bullet points.

Nice to have

• You have hands-on experience training models on large-scale GPU clusters, implementing advanced parallelism strategies, and understanding the underlying cross-node communication patterns (NCCL, MPI).

• You have worked with large-scale datasets made of different modalities. Understanding the tradeoffs in locality, encoding, formats, and streaming strategies.

• You are proficient with workload orchestrators like Slurm, Kubernetes, or Ray, having used them to schedule and manage complex, concurrent training jobs at scale.

• You understand GPU architecture at a low level: memory hierarchies, warp execution, and what makes a GPU suited for training versus inference workloads.

Join Helsing and work with world-leading experts in their fields

• Helsing’s work is important. You’ll be directly contributing to the protection of democratic countries while balancing both ethical and geopolitical concerns. 

• The work is unique. We operate in a domain that has highly unusual technical requirements and constraints, and where robustness, safety, and ethical considerations are vital. You will face unique Engineering and AI challenges that make a meaningful impact in the world. 

• Our work frequently takes us right up to the state of the art in technical innovation, be it reinforcement learning, distributed systems, generative AI, or deployment infrastructure. The defence industry is entering the most exciting phase of the technological development curve. Advances in our field of world are not incremental: Helsing is part of, and often leading, historic leaps forward. 

• In our domain, success is a matter of order-of-magnitude improvements and novel capabilities. This means we take bets, aim high, and focus on big opportunities. Despite being a relatively young company, Helsing has already been selected for multiple significant government contracts.  

• We actively encourage healthy, proactive, and diverse debate internally about what we do and how we choose to do it. Teams and individual engineers are trusted (and encouraged) to practise responsible autonomy and critical thinking, and to focus on outcomes, not conformity. At Helsing you will have a say in how we (and you!) work, the opportunity to engage on what does and doesn’t work, and to take ownership of aspects of our culture that you care deeply about. 

What we offer

• Competitive salary and VSOP options
  
  
• Relocation support: up to €2,500 and 4 weeks temporary accommodation
  
  
• Learning: €500/£450 yearly allowance
  
  
• Health & wellness: gym membership and mental health support (Nilo.health)
  
  
• Social: regular company events and monthly social allowances
  
  
• Enhanced parental leave: 22 weeks fully paid for primary caregivers & 6 weeks for secondary caregivers
  
  
• Family support: 5 days of paid family emergency leave, 100% remote work option during pregnancy and phased return to work

• A hands-on onboarding program (affectionately labelled “AI-duction”), in which you will be familiarising yourself with our tools and ML pipelines used across the company. This is your opportunity to learn our tech stack, explore the company, and learn how we get things done - all whilst working with other AI and engineering teams from day one

These are the core benefits across all locations, there may be additional benefits in certain locations.

Helsing is an equal opportunities employer. We are committed to equal employment opportunity regardless of race, religion, sexual orientation, age, marital status, disability or gender identity. Please do not submit personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, data concerning your health, or data concerning your sexual orientation. 

Who we are

The role

At Helsing, we are redefining perception by building foundational intelligence for the physical world. You will research, design, and train large-scale Foundational Models that transform complex multimodal sensor data into cutting-edge autonomous capabilities.

We are seeking an individual at the intersection of AI Research and Machine Learning Engineering, with a proven track record in LLM/VLM/(multimodal) backbones. Your primary focus will be training and fine-tuning Vision-Language Models on our custom datasets to power our different products. You'll be responsible for the entire model lifecycle, from data curation and training to evaluation.

You should apply if you

• Hold an MSc or PhD in Machine Learning, Computer Science, or Mathematics with a focus on Deep Learning.

• Possess theoretical understanding and practical experience in training Foundational Models (LLMs, VLMs, or other large-scale multimodal models) from scratch or through fine-tuning.

• Have strong software engineering skills in Python and fluency with modern DL frameworks (PyTorch/JAX). You don’t just import libraries; you are comfortable writing custom layers, loss functions, and distributed training loops.

• Are a clear communicator who can build from complex theoretical concepts and contribute to the company's internal research culture.

• Have a "first-principles" mindset: you enjoy reading the latest ArXiv papers and implementing them into the codebase rapidly.

Note: We operate at an intersection where women, as well as other minority groups, are systematically under-represented. We encourage you to apply even if you don’t meet all the listed qualifications; ability and impact cannot be summarised in a few bullet points.

Nice to have

• Top-Tier Research Track Record: You have authored publications in top-tier conferences (NeurIPS, ICML, ICLR, CVPR, ACL) specifically regarding attention mechanisms, efficient transformers, or multimodal learning.

• Experience training models on large-scale GPU clusters.

• Proven experience in data curation, data cleaning, data pruning, and building robust data pipelines for large-scale datasets.

Join Helsing and work with world-leading experts in their fields

• Helsing’s work is important. You’ll be directly contributing to the protection of democratic countries while balancing both ethical and geopolitical concerns. 

• The work is unique. We operate in a domain that has highly unusual technical requirements and constraints, and where robustness, safety, and ethical considerations are vital. You will face unique Engineering and AI challenges that make a meaningful impact in the world. 

• Our work frequently takes us right up to the state of the art in technical innovation, be it reinforcement learning, distributed systems, generative AI, or deployment infrastructure. The defence industry is entering the most exciting phase of the technological development curve. Advances in our field of world are not incremental: Helsing is part of, and often leading, historic leaps forward. 

• In our domain, success is a matter of order-of-magnitude improvements and novel capabilities. This means we take bets, aim high, and focus on big opportunities. Despite being a relatively young company, Helsing has already been selected for multiple significant government contracts.  

• We actively encourage healthy, proactive, and diverse debate internally about what we do and how we choose to do it. Teams and individual engineers are trusted (and encouraged) to practise responsible autonomy and critical thinking, and to focus on outcomes, not conformity. At Helsing you will have a say in how we (and you!) work, the opportunity to engage on what does and doesn’t work, and to take ownership of aspects of our culture that you care deeply about. 

What we offer

• Competitive salary and VSOP options

• Relocation support: up to €2,500 and 4 weeks temporary accommodation

• Learning: €500/£450 yearly allowance

• Health & wellness: gym membership and mental health support (Nilo.health)

• Social: regular company events and monthly social allowances

• Enhanced parental leave: 22 weeks fully paid for primary caregivers & 6 weeks for secondary caregivers

• Family support: 5 days of paid family emergency leave, 100% remote work option during pregnancy and phased return to work

These are the core benefits across all locations, there may be additional benefits in certain locations.

Helsing is an equal opportunities employer. We are committed to equal employment opportunity regardless of race, religion, sexual orientation, age, marital status, disability or gender identity. Please do not submit personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, data concerning your health, or data concerning your sexual orientation. 

What You’ll Do:

The Monolith Data Science team is building a layered reliability platform that shifts CoreWeave from reactive troubleshooting to proactive reliability engineering. The platform spans telemetry ingestion, feature engineering, anomaly detection, failure prediction, distributed straggler detection, and agentic root cause analysis. As a forward deployed function, we partner closely with Fleet, Infrastructure, and AI Platform teams to embed data science directly into production environments—improving cluster reliability, increasing effective utilization (MFU), reducing MTTR, and protecting uptime and revenue.

About the role:

As a Forward Deployed Data Scientist, you will work at the intersection of data science and production systems, deploying advanced statistical models and machine learning methodologies directly within operational environments. You will collaborate closely with engineering and infrastructure teams to optimize GPU utilization, workload scheduling, and system efficiency in real time. You will design experiments, analyze large-scale system telemetry data, and build predictive and optimization solutions that are tightly integrated into production workflows. This role blends hands-on deployment with analytical rigor, turning complex infrastructure data into measurable improvements in performance and cost. You will translate research and modeling insights into scalable, production-ready systems.

Who You Are:

• MS or PhD in Computer Science, Statistics, Applied Mathematics, Machine Learning, or related quantitative field
• 8+ years (or equivalent experience) applying statistical modeling or machine learning to large-scale datasets
• Strong proficiency in Python and scientific computing libraries (NumPy, pandas, SciPy, scikit-learn, PyTorch or TensorFlow)
• Demonstrated experience designing and analyzing controlled experiments (A/B testing, causal inference, hypothesis testing)
• Experience working with distributed data systems (Spark, Ray, Dask, or similar)
• Proficiency in SQL and working with large-scale structured datasets
• Experience building, deploying, and maintaining predictive models in production environments
• Strong understanding of optimization techniques (linear programming, convex optimization, stochastic optimization, or reinforcement learning)
• Experience working with time-series data and performance telemetry
• Ability to translate analytical insights into production-ready systems and workflows
• Strong collaboration skills and ability to work cross-functionally in fast-paced environments

Preferred:

• PhD with published research in systems optimization, distributed computing, ML systems, or performance modeling
• Experience with GPU workloads, distributed training, or AI infrastructure
• Familiarity with Kubernetes, containerized workloads, or cloud-native systems
• Experience deploying reinforcement learning or adaptive scheduling systems in production
• Background in capacity planning, forecasting, or resource allocation modeling
• Contributions to open-source ML or systems projects

Wondering if you’re a good fit?
We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams—even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk.

• You love uncovering hidden failure patterns in massive, noisy infrastructure datasets
• You enjoy working directly with engineering teams to deploy solutions that have immediate real-world impact
• You’re curious about building autonomous, agentic systems that investigate and explain system behavior
• You’re an expert in reinforcement learning, predictive modeling, or large-scale data analysis

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast! We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values:

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and enables the development of innovative solutions to complex problems. As we get set for takeoff, the organization's growth opportunities are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!

The starting salary will be determined by job-related knowledge, skills, experience, and the market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

What You’ll Do:

The Data Science team is focused on developing an advanced reliability platform. This system covers various aspects of data processing and analysis, including data intake, deriving meaningful metrics, identifying unusual patterns, predicting potential issues, finding slow processes in distributed systems, and using automated analysis to determine causes. We collaborate closely with internal teams like Fleet, Infrastructure, and AI Platform to enhance system stability, optimize resource use, shorten resolution times, and maintain service availability and financial performance.

About the role:

As a Senior Data & MLOps Engineer, you will design and scale the infrastructure supporting the GPU Intelligence Platform. This involves building pipelines for handling data, features, model training, and delivering insights and predictions for system health and optimization. You will transition the system from initial prototypes to a production environment operating across the fleet, focusing on scalability, separating real-time service from periodic processing, and dynamic resource management based on system load and data frequency. You will architect and deploy these scalable distributed services using orchestration technologies.

Key responsibilities:

• Design and implement scalable data ingestion pipelines.
• Build feature processing and baseline computation systems.
• Productionize models for prediction and detection.
• Develop and operate low-latency service and robust offline workflows.
• Architect horizontally scalable services with clear separation between components, leveraging orchestration for distribution.
• Implement monitoring and feedback loops for continuous model and signal improvement.
• Collaborate with Platform teams to integrate operational signals into monitoring and diagnostics.
• Implement a scalable solution for mitigation and structured analysis.

Who You Are:

• 7+ years of experience in data engineering, distributed systems, MLOps, or infrastructure ML roles in production environments.
• Proven experience building high-throughput streaming or telemetry pipelines (e.g., Kafka, Pulsar, Kinesis, or equivalent).
• Strong experience designing time-series feature pipelines and operating large-scale observability systems.
• Experience building and maintaining feature stores and ensuring offline/online feature parity.
• Hands-on experience deploying ML models to production, including versioning, monitoring, rollback, and drift detection.
• Experience designing scalable microservices deployed in Kubernetes-based environments.
• Strong proficiency in Python and at least one systems language (Go, Rust, or C++).
• Experience working with distributed compute or training systems (e.g., NCCL, PyTorch Distributed, Spark, Ray, Slurm).
• Familiarity with GPU telemetry systems such as NVML or DCGM and hardware-level monitoring concepts.
• Demonstrated experience scaling systems from Proof-of-Concept to production-grade, fleet-level deployments.

Preferred: 

• Experience working on GPU fleet management, hyperscale infrastructure, or AI training clusters.
• Experience building anomaly detection or failure prediction systems for hardware or distributed systems.
• Experience implementing distributed straggler detection or collective-level performance analysis systems.
• Experience developing agentic or LLM-powered reasoning systems for diagnostics or operational intelligence.
• Background in reliability engineering or SRE practices.

Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk. 

• You love building systems that turn raw infrastructure telemetry into actionable intelligence.
• You’re curious about distributed systems failure modes, GPU performance pathologies, and reliability engineering at scale.
• You’re excited by the idea of moving from anomaly detection to prediction to autonomous root cause reasoning.
• You enjoy designing platforms that protect uptime, revenue, and customer trust through proactive systems thinking.

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and enables the development of innovative solutions to complex problems. As we get set for takeoff, the organization's growth opportunities are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us! 

About us:

• We are a multinational company with offices in the US (New York and Los Angeles), Germany (Berlin), and the UK (London)
• We provide a flexible work culture that offers the opportunity to collaborate and connect in person at our offices as well as accommodating work from home
• We are deeply committed to ensuring diversity, equity and inclusion at all levels of our organization and fostering a community where everyone’s voice, perspective and experience is respected and heard.
• We believe a strong team is made by investing in employees through mentorship, workshops and enrichment opportunities

Benefits:

• Not located in Berlin? No worries, we offer extensive relocation support including allowances, one way flights, temporary accommodation and, by partnering with Expath, on the ground support on arrival
• Interested in a gym membership, photography course or book? We have a Creativity and Wellness benefit!
• Employee Equity Plan
• Generous professional development allowance
• Flexible vacation and public holiday policy where you can take up to 35 days of PTO annually
• We offer free German courses at beginning, intermediate and advanced
• Various snacks, goodies, and 2 free lunches weekly when at the office

Diversity, Equity and Inclusion at SoundCloud

SoundCloud is for everyone. Diversity and open expression are fundamental to our organization; they help us lead what’s next in music by understanding and empowering our creators and fans, no matter their identity. We acknowledge the challenges in the music industry, and strive to influence an inclusive culture where everyone can contribute respectfully and thrive, especially the historically marginalized communities that many of our creators, fans and SoundClouders identify with. We are dedicated to creating an inclusive environment at SoundCloud for everyone, regardless of gender identity, sexual orientation, race, ethnicity, migration background, national origin, age, disability status, or care-giver status.

At SoundCloud you can find your community or elevate your allyship by joining a Diversity Resource Group. Diversity Resource Groups are employee-organized groups focused on supporting and promoting the interests of a particular underrepresented community in order to build a more inclusive culture at SoundCloud. Anyone can join, whether you share the identity or strive to be an ally.

About us:

• We are a multinational company with offices in the US (New York and Los Angeles), Germany (Berlin), and the UK (London)
• We provide a flexible work culture that offers the opportunity to collaborate and connect in person at our offices as well as accommodating work from home
• We are deeply committed to ensuring diversity, equity and inclusion at all levels of our organization and fostering a community where everyone’s voice, perspective and experience is respected and heard
• We believe a strong team is made by investing in employees through mentorship, workshops and enrichment opportunities

Benefits:

• Comprehensive health benefits including medical, dental, and vision plans, as well as mental health resources
• Robust 401k program
• Employee Equity Plan
• Generous professional development allowance
• Interested in a gym membership, photography course or book? We have a Creativity and Wellness benefit!
• Flexible vacation and public holiday policy where you can take up to 35 days of PTO annually
• 16 paid weeks for all parents (birthing and non-birthing), regardless of gender, to welcome newborns, adopted and foster children
• Various snacks, goodies, and 2 free lunches weekly when at the office

Diversity, Equity and Inclusion at SoundCloud

SoundCloud is for everyone. Diversity and open expression are fundamental to our organization; they help us lead what’s next in music by understanding and empowering our creators and fans, no matter their identity. We acknowledge the challenges in the music industry, and strive to influence an inclusive culture where everyone can contribute respectfully and thrive, especially the historically marginalized communities that many of our creators, fans and SoundClouders identify with. We are dedicated to creating an inclusive environment at SoundCloud for everyone, regardless of gender identity, sexual orientation, race, ethnicity, migration background, national origin, age, disability status, or care-giver status.

At SoundCloud you can find your community or elevate your allyship by joining a Diversity Resource Group. Diversity Resource Groups are employee-organized groups focused on supporting and promoting the interests of a particular underrepresented community in order to build a more inclusive culture at SoundCloud. Anyone can join, whether you share the identity or strive to be an ally.

What We're Looking For

We are seeking a highly skilled Research Engineer to work on optimizing training and inference workloads on compute accelerators and clusters, through the Lightning Thunder compiler and the broader PyTorch Lightning ecosystem. This role sits at the intersection of deep learning research, compiler development, and large-scale system optimization. You’ll be shaping technology that pushes the boundaries of model performance and efficiency, creating foundational software that will impact the entire machine learning ecosystem.

This role is based in one of our hubs (NYC, SF, Seattle, or London), with a minimum of 2 in-office days per week and occasional team and company offsites.

What You'll Do

• Develop performance-oriented model optimizations at multiple levels:
• Graph-level (e.g., operator fusion, kernel scheduling, memory planning)
• Kernel-level (CUDA, Triton, custom operators for specialized hardware)
• System-level (distributed training across GPUs/TPUs, inference serving at scale)
• Advance the Thunder compiler by building optimization passes, graph transformations, and integration hooks to accelerate training and inference workloads.
• Work across the software stack to ensure optimizations are accessible to end users through clean APIs, automated tooling, and seamless integration with PyTorch Lightning.
  Design and implement profiling and debugging tools to analyze model execution, identify bottlenecks, and guide optimization strategies.
• Collaborate with hardware vendors and ecosystem partners to ensure Thunder runs efficiently across diverse backends (NVIDIA, AMD, TPU, specialized accelerators).
• Contribute to open-source projects by developing new features, improving documentation, and supporting community adoption.
• Engage with researchers and engineers in the community, providing guidance on performance tuning and advocating for Thunder as the go-to optimization layer in ML workflows.
• Work cross-functionally with Lightning’s product and engineering teams to ensure compiler and optimization improvements align with the broader product vision.

What You’ll Need

• Strong expertise with deep learning frameworks such as PyTorch
• Hands-on experience with model optimization techniques, including graph-level optimizations, quantization, pruning, mixed precision, or memory-efficient training.
• Knowledge of distributed systems and parallelism strategies (data/model/pipeline parallelism, checkpointing, elastic scaling).
• Familiarity with software engineering practices: designing APIs, building robust tooling, testing, CI/CD for performance-sensitive systems.
• Excellent collaboration and communication skills, with the ability to partner across research, engineering, and external contributors.
• Bachelor’s degree in Computer Science, Engineering, or a related field.

Nice-to-Haves

• Experience with CUDA, Triton, or other GPU programming models for developing custom kernels.
• Deep understanding of deep learning compiler internals (IR design, operator fusion, scheduling, optimization passes) or proven work in performance-critical software.
• Proven track record contributing to open-source projects in ML, HPC, or compiler domains.
• Advanced degree (Master’s or PhD) in machine learning, compilers, or systems highly preferred.

Compensation

**This is a hybrid position open to candidates in the London area, only requiring 2 days in the office**

We’re looking for a Senior Data Scientist to help build, scale, and operate production‑grade machine learning systems that power a high‑growth SaaS platform. In this role, you’ll take models from experimentation to real‑world impact—supporting real‑time decisioning, predictive analytics, and data‑driven product capabilities used by customers at scale. 

You’ll work closely with engineering, product, and data teams to deliver ML solutions that are reliable, observable, and performant in a multi‑tenant SaaS environment. 

HOW WE’LL BE WINNING TOGETHER DAY TO DAY

• Prediction models — Training and optimizing neural networks to predict eCommerce events across massive datasets 
• Real‑time inference — Designing and deploying low‑latency models with millisecond‑level time budgets 
• Production ML at scale — Building and operating ML models and web services handling 1,000+ RPS

WE SHOULD TALK IF THIS SOUNDS LIKE YOU \

• 4+ years of experience building machine learning models, with a strong emphasis on neural networks
• Strong proficiency in Python, including notebooks and production‑grade code 
• Experience in training and deploying neural network models in production environments 
• Hands‑on experience with ML frameworks such as PyTorch and TensorFlow 
• Experience building web services and APIs using Flask, FastAPI, or similar frameworks 
• Comfortable working in Linux environments, including shell scripting for monitoring and debugging applications 

DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.

Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.

We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.

As an AI Engineer at DRW, you will be an integral member of a collaborative multi-asset trading team. You’ll work on high-impact machine learning (ML) and artificial intelligence (AI) initiatives that are central to our business strategy. In this role, you will build critical infrastructure and tooling that leverages AI and ML technology to enhance productivity, efficiency, and discover innovative paths to challenging business and technology problems. Surrounded by cutting-edge technology and experienced trading teams, you will have the opportunity to tackle fascinating new projects while becoming a subject-matter expert in a critical and innovative role.

Key Responsibilities:

• Drive end-to-end development of AI infrastructure and AI driven applications: from initial proof-of-concept to production deployment and ongoing maintenance.
• Collaborate with technologists, traders, quantitative researchers, and data scientists to identify high-impact opportunities for integrating AI and machine learning into technology and business use cases.
• Implement automated systems for continuous training, validation, and monitoring of models, minimizing downtime and ensuring reliability.
• Provide technical leadership in selecting, integrating, and optimizing AI and ML frameworks, libraries, and tools across diverse hardware and software environments.
• Create and maintain feature pipelines, feature stores, and model stores.
• Develop frameworks to enable scalable, reproducible research.
• Proactively troubleshoot performance bottlenecks, conduct root-cause analyses, and implement solutions to optimize GPU or CPU resource usage.

Qualifications:

• Bachelor’s or advanced degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• 3+ years of experience working with machine learning and artificial intelligence technology.
• Strong understanding of core machine learning and artificial intelligence concepts.
• Excellent programming skills in Python.
• Demonstrated experience in building, validating, deploying, monitoring, and updating production ML and AI models.
• Hands-on experience with MLOps and AIOps infrastructure and tooling.
• Proficient in problem-solving and analytical reasoning.
• Exceptional communication and collaboration skills.
• Experience with ML frameworks such as TensorFlow, PyTorch, TensorRT, or ONNX.
• Experience with Large Language Models, including RAG and fine-tuning techniques.
• Familiarity with compute infrastructure necessary to support operating AI and ML technology.

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#LI-BL1

Snapshot

We are seeking a Manager, Applied AI to lead the development and deployment of novel applications, leveraging Google’s generative AI models. This role focuses on rapidly developing new features, and working across partner teams to deliver solutions, and maximize impact for Google & top Google customers. You will be instrumental in translating cutting-edge AI research into real-world products, and demonstrating the capabilities of latest generation models. We are looking for a manager with a strong track record of building and shipping software, and leading engineering teams, ideally with some experience in early-stage environments, where you may have contributed to scaling products from initial concept to production. The ideal candidate will be motivated by the opportunity to drive product & business impact, and develop strong relationships with research teams.

About us

Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.

The role

This role requires a strong software engineering foundation, a passion for building and iterating on software products, and proven leadership skills. We are looking for a manager who thrives in fast-paced environments, and has the proven ability to deliver high-quality code & guide a team to success. Experience in end-to-end development is essential, as is the ability to mentor and develop junior engineers. We particularly value experience in early-stage or startup environments, where managers take ownership and drive product development from the ground up.

Key responsibilities

• Lead a team in the design and development of scalable software applications leveraging generative AI models.
• Guide the team in rapidly developing new features and iterating based on evaluation results.
• Collaborate with researchers and product managers to translate research advancements into tangible product features.
• Oversee the optimization of software performance and ensure the reliability of deployed applications.
• Champion the development of best practices for building and deploying generative AI applications.
• Lead the architecture and development of new products & features from 0 to 1.
• Mentor and develop team members, fostering a collaborative and high-performing environment.
• Partner with PM team to qualify & size new project opportunities.

About you

In order to set you up for success as a Manager, Applied AI at Google DeepMind, we look for the following skills and experience:

Required Skills

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript).
• 5 years of experience in a technical leadership role; overseeing projects, with 5 years of experience in a people management, supervision/team leadership role.
• 5 years of experience with one or more of the following: media generation, reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
• 7 years of experience leading technical project strategy, ML design, and optimizing industry-scale ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• Proven experience managing software engineering teams.
• Strong software engineering skills; proficiency in Python.
• Proven experience in developing and shipping software products rapidly.
• Experience evaluating model performance, analyzing results, and implementing improvements.
• Experience with cloud computing platforms and infrastructure (e.g., Google Cloud Platform, AWS, Azure).
• Substantial experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Hugging Face, etc.
• Ability to work in a fast-paced environment and adapt to changing priorities.
• Excellent communication, collaboration, and leadership skills.

Preferred Skills

• Experience with generative AI research or applications.
• Contributions to open-source projects.
• Experience with front-end development.
• Experience working in, or founding early-stage startups.
• Experience delivering software solutions in a fast-paced, customer-facing environment.

If you are a passionate machine learning engineering leader with a drive to build innovative products and a desire to work at the forefront of AI, we encourage you to apply!

At Google DeepMind, we value diversity of experience, knowledge, backgrounds, and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.

What We're Looking For

We are seeking a highly skilled Lead Research Engineer to work on optimizing training and inference workloads on compute accelerators and clusters, through  the Lightning Thunder compiler and the broader PyTorch Lightning ecosystem. This role sits at the intersection of deep learning research, compiler development, and large-scale system optimization. You’ll be shaping technology that pushes the boundaries of model performance and efficiency, creating foundational software that will impact the entire machine learning ecosystem.

This role is based in one of our hubs (NYC, SF, or London), with a minimum of 2 in-office days per week and occasional team and company offsites.

What You’ll Do

• Develop performance-oriented model optimizations at multiple levels:
• Graph-level (e.g., operator fusion, kernel scheduling, memory planning)
• Kernel-level (CUDA, Triton, custom operators for specialized hardware)
• System-level (distributed training across GPUs/TPUs, inference serving at scale)
• Advance the Thunder compiler by building optimization passes, graph transformations, and integration hooks to accelerate training and inference workloads.
• Work across the software stack to ensure optimizations are accessible to end users through clean APIs, automated tooling, and seamless integration with PyTorch Lightning.
  Design and implement profiling and debugging tools to analyze model execution, identify bottlenecks, and guide optimization strategies.
• Collaborate with hardware vendors and ecosystem partners to ensure Thunder runs efficiently across diverse backends (NVIDIA, AMD, TPU, specialized accelerators).
• Contribute to open-source projects by developing new features, improving documentation, and supporting community adoption.
• Engage with researchers and engineers in the community, providing guidance on performance tuning and advocating for Thunder as the go-to optimization layer in ML workflows.
• Work cross-functionally with Lightning’s product and engineering teams to ensure compiler and optimization improvements align with the broader product vision.

What You’ll Need

• Strong expertise with deep learning frameworks such as PyTorch
• Hands-on experience with model optimization techniques, including graph-level optimizations, quantization, pruning, mixed precision, or memory-efficient training.
• Knowledge of distributed systems and parallelism strategies (data/model/pipeline parallelism, checkpointing, elastic scaling).
• Familiarity with software engineering practices: designing APIs, building robust tooling, testing, CI/CD for performance-sensitive systems.
• Excellent collaboration and communication skills, with the ability to partner across research, engineering, and external contributors.
• Bachelor’s degree in Computer Science, Engineering, or a related field

Nice-to-Haves

• Experience with CUDA, Triton, or other GPU programming models for developing custom kernels.
• Deep understanding of deep learning compiler internals (IR design, operator fusion, scheduling, optimization passes) or proven work in performance-critical software.
• Proven track record contributing to open-source projects in ML, HPC, or compiler domains.
• Advanced degree (Master’s or PhD) in machine learning, compilers, or systems highly preferred.

Compensation

We're seeking a versatile Senior GenAI Engineer who can work across the full stack of GenAI applications, from model integration and prompt engineering to building intuitive user interfaces. You'll build production-ready AI features that empower business users to leverage the power of GenAI within their planning workflows, requiring both deep ML knowledge and strong software engineering skills. 

Your Impact  

• Develop end-to-end GenAI features, including backend API services, model integration, model monitoring, evaluations, and deployments 
• Integrate and optimize LLMs for specific use cases in business planning, including prompt engineering and RAG implementation 
• Build conversational interfaces and agentic workflows that make complex planning tasks accessible through natural language 
• Implement evaluation frameworks to measure and improve GenAI feature quality, including accuracy, latency, and user satisfaction metrics 
• Design and develop APIs that expose AI capabilities to Anaplan's platform and third-party integrations 
• Optimize model inference pipelines for performance, cost, and scalability in production environments 
• Implement monitoring, logging, and observability for GenAI systems to track usage, errors, and model behaviour 
• Collaborate with data scientists to productionize ML models and forecasting algorithms 
• Write comprehensive tests, including unit tests, integration tests, and prompt regression suites 
• Participate in code reviews, technical design discussions, and knowledge-sharing sessions 
• Stay current with GenAI research and tools, evaluating new models and techniques for potential adoption 

 Your Qualifications 

• Software engineering experience with a focus on ML/AI systems 
• Strong programming skills in Python, including experience with ML frameworks (PyTorch, TensorFlow, Transformers) 
• Experience building and deploying LLM-powered applications in production 
• Proficiency in front-end development with React, TypeScript, and modern web technologies 
• Understanding of RESTful API design, microservices architecture, and cloud infrastructure 
• Experience with prompt engineering, RAG systems 
• Strong foundation in ML fundamentals, including NLP, time-series analysis, or recommender systems 
• Familiarity with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines 
• Excellent problem-solving skills and attention to detail 
• Bachelor's degree in computer science, Machine Learning, or related field 

Preferred Qualifications 

• Experience with GenAI frameworks (LangChain, LlamaIndex, Haystack, or similar) 
• Knowledge of vector databases (Pinecone, Weaviate, Qdrant) and embedding models 
• Experience with model serving frameworks (vLLM, TensorRT, Ray) 
• Background in forecasting, planning, or analytics applications 
• Familiarity with Anaplan or similar enterprise planning platforms 
• Experience with A/B testing and experimentation frameworks for AI features 
• Contributions to open-source ML projects or research publications 
• Experience with model observability tools (LangSmith, W&B, MLflow) 

 What Makes This Role Exciting 

• Lead a greenfield team building transformative AI capabilities from the ground up 
• Work on cutting-edge conversational and agentic AI technologies 
• Build user-facing AI features that directly impact how businesses plan and make decisions 
• Experiment with the latest GenAI models and techniques 
• Collaborate with talented engineers, data scientists, and product designers 
• Solve unique challenges at the intersection of AI and enterprise software 
• Grow your skills in both ML engineering and full-stack development 

#LI-SP1

Your Impact

We are seeking multiple highly skilled Full-Stack members of Technical Staff (MTS) to join our CoreAI organization, driving end-to-end development of AI systems across Cloud, Edge Devices, Mission Critical and Robotics platforms.

This role goes beyond pure research. You will operate across the full stack, from data, models, and infrastructure to system integration and production deployment, to deliver scalable, real-world AI solutions. You will work on cutting-edge applications spanning computer vision, NLU, multimodal AI (MLLMs), and GenAI, enabling intelligent perception, reasoning, and action in mission-critical environments.

As a senior technical leader, you will partner closely with research scientists, machine learning engineers, hardware, firmware, and product teams to translate advanced algorithms into robust, production-ready systems that operate reliably at scale.

Why This Role

You will have the opportunity to own and shape end-to-end AI systems that operate in real-world environments, bridging the gap between cutting-edge research and impactful production deployment. This role is ideal for engineers who thrive at the intersection of AI, systems, and product, and want to build scalable, high-impact solutions across cloud and edge.

What You’ll Do
Location: any cities with Axon Engineering Hub in US, Vietnam, EU (see https://www.axon.com/company)

• EU: Finland, Belgium, UK

Responsibilities

• Own end-to-end system development across data pipelines, model development, evaluation, and deployment for AI-powered products.
• Design and build scalable, production-grade systems for real-time perception, multimodal understanding, and decision-making.
• Develop and deploy models across cloud and edge environments, including resource-constrained devices.
• Architect and optimize full-stack AI pipelines, including:
• Translate research advances in computer vision, NLU, GenAI, and multimodal LLMs into production systems.
• Collaborate cross-functionally with MLE, hardware, firmware, and product teams to integrate AI into real-world systems.
• Optimize solutions for hardware-specific acceleration (GPU, NPU, DSP) and edge deployment.
• Define and drive evaluation frameworks and metrics to ensure system performance, reliability, and safety.
• Lead technical direction for complex, ambiguous problems and influence system architecture across teams.
• Contribute to technical design docs, internal knowledge sharing, and long-term platform strategy.

Basic Qualifications

• Master’s, or PhD in Computer Science, Electrical Engineering, or related field
• 8+ years (L8) or 10+ years (L9) of industry experience building production systems
• Strong programming skills in Python/C++ and experience with modern ML frameworks (e.g., PyTorch, TensorFlow)
• Proven experience delivering end-to-end systems from concept to production
• Deep expertise in one or more areas:
• Experience building and scaling distributed systems or cloud-based ML pipelines
• Experience optimizing models for latency, cost, and deployment constraints

Preferred Qualifications

• Experience deploying AI both on Cloud (AWS, AzureML), edge devices (e.g., mobile, embedded systems, robotics platforms on Qualcomm, NVIDIA, Intel)
• Familiarity with hardware acceleration frameworks (TensorRT, ONNX, SNPE, etc.)
• Experience with real-time systems and streaming data pipelines
• Knowledge of multimodal data processing (vision, audio, text, sensor fusion)
• Experience with AWS or other cloud platforms for large-scale inference and training
• Strong system design and architecture skills across cloud ↔ edge environments
• Track record of technical leadership, mentoring, and driving cross-team initiatives
• Experience in privacy-preserving AI, security, or safety-critical systems

Benefits that Benefit You

• Competitive salary and 401k with employer match
• Discretionary paid time off
• Paid parental leave for all
• Medical, Dental, Vision plans
• Fitness Programs
• Emotional & Mental Wellness support
• Learning & Development programs
• And yes, we have snacks in our offices

Benefits listed herein may vary depending on the nature of your employment and the location where you work

Location: This role is based out of our London, England office and follows a hybrid schedule. We rely on in-person collaboration and ask that team members work onsite Tuesday through Friday, with flexibility to work remotely on Mondays. We believe connection fuels innovation, and our in-office culture is designed to support meaningful teamwork and mentorship.

As a Data Scientist supporting AI engineers, you will partner with one or more engineering teams, developing actionable insights that guide improvements to the Wayve AI Driver. Using experimental and observational analyses of real and simulated driving, you will help teams advance the functionality, safety, and performance of the Wayve AI Driver, helping to advance Wayve as the leader in end-to-end AI for autonomous mobility.

This means you might:

• Formulate and iterate upon the performance metrics that organize our engineering efforts and guide progress toward commercial success
• Design experiments and targeted off-road measurements to ensure that we deliver product requirements to customers while maintaining safety and performance
• Investigate factors in model training and inference leading to bottlenecks in functionality and performance, identifying and validating hypotheses for unlocking improvements

What we are looking for in our candidate

Essential

• 3+ years experience working in a Data Science role.
• Fluent in querying and building large datasets, writing production-level SQL for use in data-transformation pipelines.
• Prior experience designing robust real-world experiments (e.g. A/B) and critically evaluating test-statistics
• Foundations in the fundamentals behind statistics: testing appropriate distributions, testing the assumptions behind frequentist stats
• Proficient in using a statistical scripting language and data science/ML packages (e.g. python such as pandas, sklearn, statsmodels, scipy or R such as dplyr, caret, stats)
• Well-versed in summarising, visualising and communicating findings in an accessible and compelling way
• Track record of influencing team direction through your findings  
• A bias towards deriving actionable insight that can be used to drive prioritisation and strategy for others.
• Comfortable working asynchronously across time zones with cross-functional partners
• You are deeply curious about building something new and relish the idea of helping to define AV2.0 and how we build it.

Desired

• Practical experience with machine learning (e.g. pytorch). Passion to take research ideas to production.
• Track record of promoting statistical rigour and experimental best practices in your prior roles.
• Prior experience using causal inference/econometric techniques and bayesian methodologies for hypothesis testing.
• Prior experience using large datasets with distributed computing (e.g. spark, hadoop or other map-reduce tech)
• Experience working in a fast-moving tech company or startup.

We are looking for an engineer with robust experience in machine learning and strong mathematical foundations to join our growing ML team and to help drive the direction of our ML platform.

Machine learning is a critical pillar of Jane Street's global business. Our ever-evolving trading environment serves as a unique, rapid-feedback platform for ML experimentation, allowing us to incorporate new ideas with relatively little friction. Our ML team is full of people with a shared love for the craft of software engineering, and for designing APIs and systems that are delightful to use. 

We’ll rely on your in-depth knowledge of the ML ecosystem and understanding of varying approaches — whether it’s neural networks, random forests, gradient-boosted trees or sophisticated ensemble methods — to aid decision-making so we apply the right tool for the problem at hand. Your work will also focus on enhancing research workflows to tighten our feedback cycles. Successful ML engineers will be able to understand the mechanics behind various modelling techniques, while also being able to break down the mathematics behind them.

If you’ve never thought about a career in finance, you’re in good company. Many of us were in the same position before working here. While there isn’t a fixed list of qualifications we’re looking for, if you have a curious mind and a passion for solving interesting problems, we have a feeling you’ll fit right in. 

We're looking for someone with:

• Experience building and maintaining training and inference infrastructure, with an understanding of what it takes to move from concept to production
• A strong mathematical background; Good candidates will be excited about things like optimisation theory, regularisation techniques, linear algebra and the like
• A passion for keeping up with the state of the art, whether that means diving into academic papers, experimenting with the latest hardware or reading the source of a new machine learning package
• A proven ability to create and maintain an organised research codebase that produces robust, reproducible results while maintaining ease of use
• Expertise wrangling an ML framework – we're fans of PyTorch, but we'd also love to learn what you know about Jax, TensorFlow or others
• An inventive approach and the willingness to ask hard questions about whether we're taking the right approaches and using the right tools
• Fluency in English required

If you're a recruiting agency and want to partner with us, please reach out to agency-partnerships@janestreet.com.

Note: We are currently recruiting for multiple levels and positions, however please only apply for the role that best aligns with your skillset and career goals.

What you will do 

• Work closely with our machine learning engineers, simulation engineers, and customers to translate physics and engineering challenges into mathematical problem formulations.
• Build models to predict the behaviour of physical systems using state-of-the-art machine learning and deep learning techniques.
• Own Research work-streams at different levels, depending on seniority.
• Discuss the results and implications of your work with colleagues and customers, especially how these results can address real-world problems.
• Collaborate with colleagues beyond the research team to translate your models into production-ready code.
• Communicate your work to others internally and externally as called for in paper publication venues, industry workshops, customer conversations, etc. This will involve writing for academic and non-academic audiences.
• Foster a nurturing environment for colleagues with less experience in DS / ML / Stats for them to grow and you to mentor.

What you bring to the table

• Enthusiasm about using machine learning, especially deep learning and/or probabilistic methods, for science and engineering.
• Ability to scope and effectively deliver projects.
• Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly.
• Excellent collaboration and communication skills — with teams and customers alike.
• PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or a related field, with particular expertise in any of the following:
  1. operator learning (neural operators), or other probabilistic methods for PDEs;
  2. geometric deep learning or other 3D computer vision methods for point-cloud or mesh-structured data;
  3. generative models for geometry and spatiotemporal data (VAEs, Diffusion Models, Bayesian non-parametric, scaling to large datasets, etc.).
• >2 years of experience in a data-driven role in a professional industry setting (excluding post-doc positions), with exposure to:
  • building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, PyTorch, JAX), especially including deep learning applications;
  • developing models for bespoke problem settings that involve high-dimensional data (spatiotemporal, geometric, physical);
  • iterating on network architectures and model structure, tuning and optimising for inductive biases, improved generalisability, and improved performance;
  • combining theoretical reasoning with empirical intuition to guide investigation;
  • formulating and running experiment pipelines to benchmark models and produce comparable results;
  • writing skills for communication complex technical concepts to peers and non-peers, tailoring the message for the required audience.
• Publication record in reputable venues that demonstrates mastery in your field, and in particular the domains of interest listed above. Desirable venues include (but not limited to): NeurIPS, ICML, ICLR, UAI, AISTATS, AAAI, Siggraph, CVPR or TPAMI/JMLR.

What we offer

Build what actually matters

Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind.

Learn alongside exceptional people

Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home.

Influence over hierarchy

We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected.

Sustainable pace, long-term ambition

Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our Shoreditch office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person.

And it doesn’t stop there …

🚀 Equity options - share meaningfully in the company you’re helping to build.

🏦 10% employer pension contribution - because investing in future matters.

🍽️ Free office lunches - to keep you energised and focused.

👶 Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most.

🍼 YellowNest nursery scheme - to help working parents manage childcare costs.

☀️ 25 days of Annual Leave (+ Public Holidays) - because taking time to rest matters.

🏥 Private medical insurance - 100% employee cover, giving you complete peace of mind.

💪 Wellhub Subscription - gain access to thousands of gyms, classes and wellness apps, supporting both physical and mental wellbeing.

👀 Eye tests - because good work depends on good health.

📈 Personal development - dedicated support for learning, development, and leveling up over time.

💛 Employee Assistance Programme (EAP) - confidential wellbeing support, available whenever you need it.

🚲 Bike2Work scheme and 🚆 Season ticket loan - to make getting to work easier and greener.

🚗 Octopus EV salary sacrifice - for a simpler, more sustainable way to drive electric.

🔎 Watch this space, we’re continuing to build this as we grow…

Flow Traders is looking for a Senior Research Engineer to join us in our London office. This is a unique opportunity to join a leading proprietary trading firm with an entrepreneurial and innovative culture at the heart of its business. We value quick-witted, creative minds and challenge them to make full use of their capacities. 

As a Senior Research Engineer, you will be responsible for helping to lead the development of our trading model research framework and using it to conduct research to develop models for trading in production. You'll expand the framework to become global standard way of training, consuming, combining, and transforming any data source in a data-driven systematic way. You will then partner with Quantitative Researchers to build the trading models themselves.

What you will do

• Help to lead the development and global rollout of our research framework for defining and training models through various optimization procedures (supervised learning, backtesting etc.), as well as its integration with our platform for deploying and running those models in production
• Partner with Quantitative Researchers to conduct research: test hypotheses and tune/develop data-driven systematic trading strategies and alpha signals

What you need to succeed

• Advanced degree (Master's or PhD) in Machine Learning, Statistics, Physics, Computer Science or similar
• 8+ years of hands-on experience MLOps, Research Engineering, or ML Research
• A strong background in mathematics and statistics
• Strong proficiency in programming languages such as Python, with experience in libraries like numpy, pytorch, polars, pandas, and ray
• Demonstrated experience in designing and implementing end-to-end machine learning pipelines, including data preprocessing, model training, deployment, and monitoring
• Understanding of and experience with modern software development practices and tools (e.g. Agile, version control, automated testing, CI/CD, observability)
• Understanding of cloud platforms (e. g., AWS, Azure, GCP) and containerization technologies (e. g., Docker, Kubernetes)

Flow Traders does not accept unsolicited resumes from any professional staffing or search firms. All resumes, and any other information identifying potential candidates, submitted to any employee at Flow Traders via-email, the Internet or directly without a valid and signed search agreement will be deemed free to contact by Flow Traders without any restrictions and no placement fee of any kind will be paid in the event the candidate is hired by Flow Traders.

As a Data Scientist supporting AI engineers, you will partner with one or more engineering teams, developing actionable insights that guide improvements to the Wayve AI Driver. Using experimental and observational analyses of real and simulated driving, you will help teams advance the functionality, safety, and performance of the Wayve AI Driver, helping to advance Wayve as the leader in end-to-end AI for autonomous mobility.

This means you might:

• Formulate and iterate upon the performance metrics that organize our engineering efforts and guide progress toward commercial success
• Design experiments and targeted off-road measurements to ensure that we deliver product requirements to customers while maintaining safety and performance
• Investigate factors in model training and inference leading to bottlenecks in functionality and performance, identifying and validating hypotheses for unlocking improvements

What we are looking for in our candidate

Essential

• 3+ years experience working in a Data Science role.
• Fluent in querying and building large datasets, writing production-level SQL for use in data-transformation pipelines.
• Prior experience designing robust real-world experiments (e.g. A/B) and critically evaluating test-statistics
• Foundations in the fundamentals behind statistics: testing appropriate distributions, testing the assumptions behind frequentist stats
• Proficient in using a statistical scripting language and data science/ML packages (e.g. python such as pandas, sklearn, statsmodels, scipy or R such as dplyr, caret, stats)
• Well-versed in summarising, visualising and communicating findings in an accessible and compelling way
• Track record of influencing team direction through your findings  
• A bias towards deriving actionable insight that can be used to drive prioritisation and strategy for others.
• Comfortable working asynchronously across time zones with cross-functional partners
• You are deeply curious about building something new and relish the idea of helping to define AV2.0 and how we build it.

Desired

• Practical experience with machine learning (e.g. pytorch). Passion to take research ideas to production.
• Track record of promoting statistical rigour and experimental best practices in your prior roles.
• Prior experience using causal inference/econometric techniques and bayesian methodologies for hypothesis testing.
• Prior experience using large datasets with distributed computing (e.g. spark, hadoop or other map-reduce tech)
• Experience working in a fast-moving tech company or startup.

Note: We are currently recruiting for multiple positions across different levels, however please only apply for the role that best aligns with your skillset and career goals.

What you will do 

• Work closely with our research scientists and simulation engineers to build and deliver models that address real-world physics and engineering problems.
• Design, build and optimise machine learning models with a focus on scalability and efficiency in our application domain.
• Transform prototype model implementations to robust and optimised implementations.
• Implement distributed training architectures (e.g., data parallelism, parameter server, etc.) for multi-node/multi-GPU training and explore federated learning capacity using cloud (e.g., AWS, Azure, GCP) and on-premise services.
• Work with research scientists to design, build and scale foundation models for science and engineering; helping to scale and optimise model training to large data and multi-GPU cloud compute.
• Identify the best libraries, frameworks and tools for our modelling efforts to set us up for success.
• Own Research work-streams at different levels, depending on seniority.
• Discuss the results and implications of your work with colleagues and customers, especially how these results can address real-world problems.
• Work at the intersection of data science and software engineering to translate the results of our Research into re-usable libraries, tooling and products.
• Foster a nurturing environment for colleagues with less experience in ML / Engineering for them to grow and you to mentor.

What you bring to the table

• Enthusiasm about developing machine learning solutions, especially deep learning and/or probabilistic methods, and associated supporting software solutions for science and engineering.
• Ability to work autonomously and scope and effectively deliver projects across a variety of domains.
• Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly.
• Excellent collaboration and communication skills — with teams and customers alike.
• MSc or PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, software engineering, or a related field, with a record of experience in any of the following:
  
  • Scientific computing;
  • High-performance computing (CPU / GPU clusters);
  • Parallelised / distributed training for large / foundation models.
• Ideally >2 years of experience in a data-driven role in a professional setting, with exposure to:
  • scaling and optimising ML models, training and serving foundation models at scale (federated learning a bonus);
  • distributed computing frameworks (e.g., Spark, Dask) and high-performance computing frameworks (MPI, OpenMP, CUDA, Triton);
  • cloud computing (on hyper-scaler platforms, e.g., AWS, Azure, GCP);
  • building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, PyTorch, JAX), especially including deep learning applications;
  • C/C++ for computer vision, geometry processing, or scientific computing;
  • software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps);
  • container-ization and orchestration (Docker, Kubernetes, Slurm);
  • writing pipelines and experiment environments, including running experiments in pipelines in a systematic way.

What we offer

Build what actually matters

Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind.

Learn alongside exceptional people

Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home.

Influence over hierarchy

We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected.

Sustainable pace, long-term ambition

Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our Shoreditch office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person.

And it doesn’t stop there …

🚀 Equity options - share meaningfully in the company you’re helping to build.

🏦 10% employer pension contribution - because investing in future matters.

🍽️ Free office lunches - to keep you energised and focused.

👶 Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most.

🍼 YellowNest nursery scheme - to help working parents manage childcare costs.

☀️ 25 days of Annual Leave (+ Public Holidays) - because taking time to rest matters.

🏥 Private medical insurance - 100% employee cover, giving you complete peace of mind.

💪 Wellhub Subscription - gain access to thousands of gyms, classes and wellness apps, supporting both physical and mental wellbeing.

👀 Eye tests - because good work depends on good health.

📈 Personal development - dedicated support for learning, development, and leveling up over time.

💛 Employee Assistance Programme (EAP) - confidential wellbeing support, available whenever you need it.

🚲 Bike2Work scheme and 🚆 Season ticket loan - to make getting to work easier and greener.

🚗 Octopus EV salary sacrifice - for a simpler, more sustainable way to drive electric.

🔎 Watch this space, we’re continuing to build this as we grow…

Note: We are currently recruiting for multiple positions across different levels, however please only apply for the role that best aligns with your skillset and career goals.

What you will do 

• Shape Research group strategy and culture in a significant way, especially in domains of expertise.
  • Be opinionated and formulate strategy on engineering topics relevant to our Research priorities, especially on: scaled engineering, securing compute, infrastructure stack.
  • Define necessary profiles to execute this strategy.
  • Promote effective working patterns and proactively flag issues with team dynamics to foster a productive environment.
  • Nurture younger colleagues to grow their skillset and guide their professional development.
• Own Research work-streams at a high-level to deliver outcomes.
  • Align priorities with problem stakeholders, internal and external.
  • Set the technical direction for the stream and apply judgement and taste to drive progress.
  • Plan roadmaps with clear milestones for key decisions and outcomes.
  • Organise and guide the more junior members of the team to effectively execute and deliver against this roadmap.
  • Communicate purpose and key outcomes to raise awareness across the company and create opportunities for use and deployment.
• The below activities in particular.
  • Work closely with our research scientists and simulation engineers to build and deliver models that address real-world physics and engineering problems.
  • Design, build and optimise machine learning models with a focus on scalability and efficiency in our application domain.
  • Transform prototype model implementations to robust and optimised implementations.
  • Implement distributed training architectures (e.g., data parallelism, parameter server, etc.) for multi-node/multi-GPU training and explore federated learning capacity using cloud (e.g., AWS, Azure, GCP) and on-premise services.
    • Work with research scientists to design, build and scale foundation models for science and engineering; helping to scale and optimise model training to large data and multi-GPU cloud compute.
  • Identify the best libraries, frameworks and tools for our modelling efforts to set us up for success.
  • Discuss the results and implications of your work with colleagues and customers, especially how these results can address real-world problems.
  • Work at the intersection of data science and software engineering to translate the results of our Research into re-usable libraries, tooling and products.
  • Foster a nurturing environment for colleagues with less experience in ML / Engineering for them to grow and you to mentor.

What you bring to the table

• Enthusiasm about developing machine learning solutions, especially deep learning and/or probabilistic methods, and associated supporting software solutions for science and engineering.
• Ability to work autonomously and scope and effectively deliver projects across a variety of domains.
• Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly.
• Excellent collaboration and communication skills — with teams and customers alike.
• MSc or PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, software engineering, or a related field, with a record of experience in any of the following:
  • scientific computing;
  • high-performance computing (CPU / GPU clusters);
  • parallelised / distributed training for large / foundation models.
•  4 years of experience in a data-driven role in a professional industry setting, where you have been instrumental in most of the below:
  • • scaling and optimising ML models, training and serving foundation models at scale (federated learning a bonus);
    • employing distributed computing frameworks (e.g., Spark, Dask) and high-performance computing frameworks (MPI, OpenMP, CUDA, Triton);
    • employing cloud computing (on hyper-scaler platforms, e.g., AWS, Azure, GCP);
    • building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, PyTorch, JAX), especially including deep learning applications;
    • building or using C/C++ for computer vision, geometry processing, or scientific computing;
    • following and promoting software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps);
    • container-izing and orchestrating compute tasks (Docker, Kubernetes, Slurm);
    • writing pipelines and experiment environments, including running experiments in pipelines in a systematic way.

What we offer

Build what actually matters

Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind.

Learn alongside exceptional people

Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home.

Influence over hierarchy

We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected.

Sustainable pace, long-term ambition

Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our Shoreditch office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person.

And it doesn’t stop there …

🚀 Equity options - share meaningfully in the company you’re helping to build.

🏦 10% employer pension contribution - because investing in future matters.

🍽️ Free office lunches - to keep you energised and focused.

👶 Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most.

🍼 YellowNest nursery scheme - to help working parents manage childcare costs.

☀️ 25 days of Annual Leave (+ Public Holidays) - because taking time to rest matters.

🏥 Private medical insurance - 100% employee cover, giving you complete peace of mind.

💪 Wellhub Subscription - gain access to thousands of gyms, classes and wellness apps, supporting both physical and mental wellbeing.

👀 Eye tests - because good work depends on good health.

📈 Personal development - dedicated support for learning, development, and leveling up over time.

💛 Employee Assistance Programme (EAP) - confidential wellbeing support, available whenever you need it.

🚲 Bike2Work scheme and 🚆 Season ticket loan - to make getting to work easier and greener.

🚗 Octopus EV salary sacrifice - for a simpler, more sustainable way to drive electric.

🔎 Watch this space, we’re continuing to build this as we grow…

Note: We are currently recruiting for multiple levels and positions, however please only apply for the role that best aligns with your skillset and career goals.

What you will do 

• Own Research work-streams at a high-level to deliver outcomes.
  • Align priorities with problem stakeholders, internal and external.
  • Set the technical direction for the stream and apply judgement and taste to drive progress.
  • Plan roadmaps with clear milestones for key decisions and outcomes.
  • Organise and guide the more junior members of the team to effectively execute and deliver against this roadmap.
  • Communicate purpose and key outcomes to raise awareness across the company and create opportunities for use and deployment.
• Contribute towards Research group strategy and culture.
  • Identify research areas that would be valuable to the company and champion their development, ordering wrt other research objectives.
  • Promote effective working patterns and proactively flag issues with team dynamics to foster a productive environment.
  • Nurture younger colleagues to grow their skillset and guide their professional development.
• The below activities in particular.
  • Work closely with our machine learning engineers, simulation engineers, and customers to translate physics and engineering challenges into mathematical problem formulations.
  • Build models to predict the behaviour of physical systems using state-of-the-art machine learning and deep learning techniques.
  • Discuss the results and implications of your work with colleagues and customers, especially how these results can address real-world problems.
  • Collaborate with colleagues beyond the research team to translate your models into production-ready code.
  • Communicate your work to others internally and externally as called for in paper publication venues, industry workshops, customer conversations, etc. This will involve writing for academic and non-academic audiences.

What you bring to the table

• Ability to scope and effectively deliver projects.
• Enthusiasm about using machine learning, especially deep learning and/or probabilistic methods, for science and engineering.
• Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly.
• Excellent collaboration and communication skills — with teams and customers alike.
• PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or a related field, with particular expertise in any of the following:
  1. operator learning (neural operators), or other probabilistic methods for PDEs;
  2. geometric deep learning or other 3D computer vision methods for point-cloud or mesh-structured data;
  3. generative models for geometry and spatiotemporal data (VAEs, Diffusion Models, Bayesian non-parametric, scaling to large datasets, etc.).
• Ideally, >4 years of experience in a data-driven role in a professional industry setting, where you have been instrumental in:
  • building machine learning models and pipelines in Python, using common libraries and frameworks (PyTorch / CUDA, ideally with exposure to JAX, NumPy / SciPy), especially including deep learning applications;
  • developing models for bespoke problem settings that involve high-dimensional data (spatiotemporal, geometric, physical);
  • iterating on network architectures and model structure, tuning and optimising for inductive biases, improved generalisability, and improved performance;
  • combining theoretical reasoning with empirical intuition to guide investigation;
  • formulating and running experiment pipelines to benchmark models and produce comparable results;
  • writing skills for communication complex technical concepts to peers and non-peers, tailoring the message for the required audience.
• Publication record in reputable venues that demonstrates mastery in your field, and in particular the domains of interest listed above. Desirable venues include (but not limited to): NeurIPS, ICML, ICLR, UAI, AISTATS, AAAI, Siggraph, CVPR or TPAMI/JMLR.

What we offer

Build what actually matters

Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind.

Learn alongside exceptional people

Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home.

Influence over hierarchy

We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected.

Sustainable pace, long-term ambition

Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our Shoreditch office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person.

And it doesn’t stop there …

🚀 Equity options - share meaningfully in the company you’re helping to build.

🏦 10% employer pension contribution - because investing in future matters.

🍽️ Free office lunches - to keep you energised and focused.

👶 Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most.

🍼 YellowNest nursery scheme - to help working parents manage childcare costs.

☀️ 25 days of Annual Leave (+ Public Holidays) - because taking time to rest matters.

🏥 Private medical insurance - 100% employee cover, giving you complete peace of mind.

💪 Wellhub Subscription - gain access to thousands of gyms, classes and wellness apps, supporting both physical and mental wellbeing.

👀 Eye tests - because good work depends on good health.

📈 Personal development - dedicated support for learning, development, and leveling up over time.

💛 Employee Assistance Programme (EAP) - confidential wellbeing support, available whenever you need it.

🚲 Bike2Work scheme and 🚆 Season ticket loan - to make getting to work easier and greener.

🚗 Octopus EV salary sacrifice - for a simpler, more sustainable way to drive electric.

🔎 Watch this space, we’re continuing to build this as we grow…

Research Scientist in Science

Founded in 2014, InstaDeep is today an EMEA leader in decision-making AI products for the Enterprise, with headquarters in London, and offices in Paris, Berlin, Tunis, Lagos, Dubai, Cape Town and the USA. InstaDeep has been named among the Top 100 global AI startups for three consecutive years by CB Insights, as well as one of the 100 most promising B2B companies in Europe, by Sifted. We have created an environment that ensures a challenge by working closely with a broad spectrum of high-quality clients, pushing you to thrive in an environment that’s rarely tedious and always looking to push you to come up with outside-the-box solutions using cutting-edge technologies. In our mission to stay ahead of the curve, we are proud to partner with firms such as BioNTech SE, Google DeepMind, NVIDIA and Intel, and world-class universities such as MIT, Oxford, UCL, and Imperial College London. We are also an NVIDIA Elite Service Delivery Partner and Google Cloud Partner.

Are you a Research Scientist in Science? You will contribute to knowledge in different fields, and plan and conduct experiments to help expand the canon of the company's scientific knowledge.

If so then keep reading…. 

As a Research Scientist in Science, you’ll be a part of the Research department, 

The Research Scientist in Science must accomplish the following:  

• Collaborate with other researchers and experts to develop machine-learning methods for problems in computational biology.
• Engage and assist in the identification of research directions
• Contribute to the design and implementation of models, tools and libraries to accelerate our research effort.
• Adapt neural network algorithms to deploy on supercomputing architectures to best exploit modern parallel environments 
• Report and present experimental results and research findings clearly and effectively, both internally and externally, verbally and in writing. 
• Contribute to the team’s publication efforts.
• Bridge the gap between the research and product teams by integrating new fundamental research into applied projects. 

Requirements:  

• Ph.D. degree in Computer Science, Machine Learning or a related field.
• Experience using Deep Learning frameworks such as PyTorch, Tensorflow and/or Jax.
• Experience in developing and debugging in Python3 or similar languages.
• Experience with high-performance computing methods and frameworks such as MPI, Ray (distribution on clusters of CPUs and accelerators)
• Experience in planning and conducting research works
• A demonstrated ability to successfully deliver high-quality research, including publishing scientific papers (ICLR, NeurIPS, ICML, etc).
• Excellent communication skills and collaborative spirit.

Nice to haves

• 2-5 years experience

• Software engineering experience demonstrated via previous work, and contributions to open source or coding competitions.
• Reinforcement Learning, Computer Vision, or Natural Language Processing experience.

Our Diversity, Equity, and Inclusion commitments

We empower individuals to celebrate their uniqueness here at InstaDeep. Our team comes from all walks of life, and we’re proud to continue encouraging and supporting applicants from underrepresented groups across the globe. Our commitment to creating an authentic environment comes from our ability to learn and grow from our diversity, and how better to experience this by other than by joining our team? 

At present, we operate in a hybrid work model of two days mandatory a week in the office. We are continuing to review the situation with the well-being of InstaDeepers at the forefront of our minds.

Right to work: Please note you will require the legal right to work in the location you are applying for. 

If you have any further questions please do not hesitate to contact us at: talent@instadeep.com

About the role

The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster. This is high-leverage work: small improvements to velocity compound across every researcher and every run.

Responsibilities

• Build and improve the RL training infrastructure that researchers depend on day-to-day
• Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed
• Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster
• Own the reliability and performance of research runs end-to-end
• Contribute to design decisions that shape how Anthropic does RL at scale

You may be a good fit if you

• Have strong software engineering fundamentals and a track record of building performant, reliable systems
• Have worked on ML infrastructure, distributed systems, or research tooling
• Care about enabling other people's work and find leverage through platforms rather than individual experiments
• Are comfortable operating across the stack, from low-level performance work to RL algorithms
• Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego

Strong candidates may also have

• Experience with large-scale distributed training (RL, pre-training, or post-training)
• Familiarity with JAX, PyTorch, or similar ML frameworks
• A track record of operating at the edge of research and infra in a fast-moving environment

Deadline to apply: None. Applications will be reviewed on a rolling basis.

Note: We are currently recruiting for multiple positions, however please only apply for the role that best aligns with your skillset and career goals.

The Role

The Principal ML Infrastructure Engineer will extend and operate the infrastructure that powers our research model training, fine-tuning, and serving pipelines. You will be embedded within our Research function, partnering directly with ML engineers and research scientists to ensure they can train Large Physics Models efficiently and reliably at scale.

Team Context

In this role, you will be vertically embedded in Research, working daily with:

• Research Scientists who determine the model architectures and methods
• ML Engineers who implement and develop the models
• Simulation Data Engineers who are accountable for upstream data pipelines

You will have end-to-end responsibilities over the research infrastructure, with the autonomy to make architectural decisions and the responsibility to keep data flowing reliably.

Horizontally, you will be part of an infrastructure engineering group responsible for infrastructure across the company.

What you will do

Training Infrastructure

• Design and operate distributed training infrastructure for neural operator architectures (Transolver, Point Cloud Transformer, etc.) on our large NVIDIA DGX B200 platform.
• Optimize training pipelines for throughput, fault tolerance, and cost efficiency, including checkpointing strategies, gradient accumulation, and multi-node synchronization.
• Build and maintain experiment tracking and observability systems that give researchers clear visibility into training runs, hyperparameter sweeps, and model performance.

Data I/O and Performance

• Solve data loading bottlenecks for large-scale mesh datasets.
• Optimize data pipelines for efficient I/O from cloud storage, including prefetching, caching, and format optimization.
• Work with heterogeneous data sources of varying formats and resolutions.

Model Serving and Deployment

• Build serving infrastructure for pre-trained LPMs, supporting both zero-shot inference and uncertainty quantification (Monte Carlo Dropout).
• Design and implement model packaging pipelines for customer deployment. Models must run reliably in customer environments with fine-tuning capabilities.
• Ensure reproducibility: any model checkpoint should be deployable with consistent behaviour.

Platform and Tooling

• Improve developer experience for the Research team with fast iteration cycles, reliable CI/CD, clear debugging tools.
• Collaborate with the broader Infrastructure team on shared patterns and standards.

What you bring to the table

• Ability to scope and effectively deliver projects, prioritising activity as needed.
• Problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly.
• Excellent collaboration and communication skills, especially in a research setting. You can translate "the model isn't converging" into infrastructure hypotheses and solutions, and can bridge technical abstractions with implementations.
• 5+ years of experience building and operating ML infrastructure at scale:
  • Deep expertise in distributed training: you've debugged NCCL hangs, optimized collective communication, and know when to use FSDP vs. DDP vs. pipeline parallelism
  • Strong systems fundamentals: Linux, networking (including domain specific NVLink and InfiniBand), storage I/O, profiling and performance optimization
  • Production experience with Kubernetes and SLURM for job orchestration on GPU clusters
  • Proficiency in Python and ML frameworks (PyTorch strongly preferred)
  • Experience with cloud GPU infrastructure; ideally CoreWeave or similar GPU/HPC-focused clouds

Ideally

• Experience with geometric deep learning or neural operators, ****architectures that operate on meshes, point clouds, or graphs
• Background in HPC for simulation engineering, familiarity with how CFD/FEA workflows generate and consume data
• Experience building model serving infrastructure with latency and throughput requirements
• Familiarity with experiment tracking tools (Weights & Biases, MLflow) and observability stacks (Prometheus, Grafana)
• Experience packaging models for deployment into customer environments (containers, model registries, versioning)

What we offer

Build what actually matters

Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind.

Learn alongside exceptional people

Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home.

Influence over hierarchy

We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected.

Sustainable pace, long-term ambition

Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our Shoreditch office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person.

And it doesn’t stop there …

🚀 Equity options - share meaningfully in the company you’re helping to build.

🏦 10% employer pension contribution - because investing in future matters.

🍽️ Free office lunches - to keep you energised and focused.

👶 Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most.

🍼 YellowNest nursery scheme - to help working parents manage childcare costs.

☀️ 25 days of Annual Leave (+ Public Holidays) - because taking time to rest matters.

🏥 Private medical insurance - 100% employee cover, giving you complete peace of mind.

💪 Wellhub Subscription - gain access to thousands of gyms, classes and wellness apps, supporting both physical and mental wellbeing.

👀 Eye tests - because good work depends on good health.

📈 Personal development - dedicated support for learning, development, and leveling up over time.

💛 Employee Assistance Programme (EAP) - confidential wellbeing support, available whenever you need it.

🚲 Bike2Work scheme and 🚆 Season ticket loan - to make getting to work easier and greener.

🚗 Octopus EV salary sacrifice - for a simpler, more sustainable way to drive electric.

🔎 Watch this space, we’re continuing to build this as we grow…