Staff Machine Learning Software Engineer - Research

physicsx is seeking a Staff Machine Learning Software Engineer to join its Research team in London. This position centers on building advanced algorithms and machine learning solutions to drive forward ambitious research initiatives. Role overview The main focus is on designing and implementing machine learning models that push the boundaries of current technology. Projects will involve exploring new methods and supporting research efforts that aim to solve complex problems. Location This role is based in London, United Kingdom. What you will do Develop and refine machine learning algorithms for research applications Collaborate with researchers to support innovative projects Work on solutions intended to advance the capabilities of existing technology

PhysicsX is looking for a Machine Learning Software Engineer to join its research division in London. This position centers on developing new machine learning solutions in collaboration with researchers and engineers. Role overview The work involves applying advanced algorithms and data analysis techniques to address complex challenges spanning several fields. The team values creative problem-solving and technical depth. Collaboration Expect to work closely with colleagues from diverse backgrounds, combining expertise to create practical and innovative solutions. The environment encourages sharing ideas and learning from each other. Location This role is based in London, United Kingdom.

About AnthropicAt Anthropic, we are dedicated to developing reliable, interpretable, and controllable AI systems. Our goal is to ensure that AI technology is safe and beneficial for both users and society. Our rapidly expanding team consists of passionate researchers, engineers, policy experts, and business leaders collaborating to create advantageous AI systems.About the TeamsThe Reinforcement Learning teams at Anthropic spearhead our research and development in reinforcement learning, playing an essential role in enhancing our AI systems. We have made significant contributions to all Claude models, particularly impacting the autonomy and coding capabilities of Claude Sonnet 4.5 and Opus 4.5. Our work encompasses several critical areas:Creating systems that empower models to utilize computers effectively.Enhancing code generation through reinforcement learning techniques.Conducting pioneering RL research for large language models.Establishing scalable RL infrastructure and training methodologies.Improving model reasoning capabilities.We work closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and secure. Additionally, we collaborate with the applied production training team to seamlessly integrate research advancements into deployed models, demonstrating our commitment to implementing research at scale. Our Reinforcement Learning teams operate at the intersection of cutting-edge research and engineering excellence, dedicated to building high-quality, scalable systems that expand the possibilities of AI.About the RoleAs a Research Engineer in the Reinforcement Learning domain, you will partner with a diverse group of researchers and engineers to enhance the capabilities and safety of large language models. This position merges research and engineering responsibilities, requiring you to implement innovative approaches while contributing to the research strategy. You will engage in fundamental research in reinforcement learning, developing 'agentic' models capable of tool use for open-ended tasks such as computer usage and autonomous software generation, improving reasoning skills in disciplines like mathematics, and creating prototypes for internal applications, productivity, and evaluation.Representative Projects:Design and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters, scaling our systems to manage increasingly complex research workflows.Invent, implement, and evaluate novel training environments, evaluations, and methodologies for reinforcement learning.

Join Our Team as a Staff Machine Learning EngineerBecome an integral part of Deliveroo’s mission to revolutionize the shopping and dining experience with a focus on impact, innovation, and growth. Our talented Engineering teams tackle intricate technical challenges within a global, three-sided marketplace, developing and scaling systems that cater to millions of customers, riders, and partners daily.From advanced real-time logistics to robust infrastructure and marketplace optimization, we create and manage the technology that fuels Deliveroo’s expansive growth.We are seeking a Staff Machine Learning Engineer to join our dynamic London team (working in a hybrid model, 3 days in the office). In this pivotal role, you will design and construct intelligent decision-making systems that operate at a large scale, directly influencing the experiences of consumers, riders, and merchants.Explore our Engineering team and discover our motivations, work culture, and what you can expect as part of our community.Your ResponsibilitiesJoin the Consumer Pricing team to tackle complex pricing challenges at Deliveroo. We are advancing towards dynamic, personalized pricing strategies that take into account consumer behavior, loyalty programs, and real-time market conditions.Your daily tasks will include:Designing and developing high-performance machine learning and optimization systems that guide Deliveroo’s core decisions at scale.Leading the technical advancement of our pricing infrastructure, resolving architectural bottlenecks and integrating enhanced model inputs and decision variables.Creating algorithms to enhance real-time marketplace efficiency, including personalized elasticity models and delivery time forecasts.Collaborating closely with Product Managers and Data Scientists to convert complex business challenges into effective algorithmic solutions.Providing technical leadership across various product domains, identifying and prioritizing high-impact algorithmic enhancements.Mentoring and guiding fellow engineers, elevating technical standards and advancing our machine learning and optimization practices across the company.Qualifications for SuccessThe ideal candidate will possess strong expertise in several of the following areas, along with a desire to grow in others:

Anthropic seeks a Research Engineer specializing in Machine Learning, with a focus on Reinforcement Learning (RL Velocity), for its London office. This role supports ongoing AI research and contributes to building advanced machine learning systems. Key responsibilities Work alongside researchers and engineers to solve complex reinforcement learning problems Participate in designing and developing new machine learning models and systems Shape solutions that directly influence Anthropic’s research objectives Collaboration and team environment Join a team of skilled colleagues dedicated to AI advancement. Team members regularly exchange ideas, review each other's work, and support one another to create effective solutions.

About UsEstablished in the United States in 2022 and now operating from London, UK, Recraft is an innovative AI platform tailored for designers, illustrators, and marketers, setting a new benchmark in image generation excellence.Our cutting-edge tool empowers creators to swiftly generate and refine original images, vector art, illustrations, icons, and 3D graphics using advanced AI technology. With over 3 million users in 200 countries, our community has produced hundreds of millions of images with Recraft, and we are just beginning our journey.Become part of a world filled with professional opportunities, contribute to large-scale projects, and be a pioneer in the creative industry’s future. We are dedicated to making Recraft a vital tool for every designer, striving to set the industry standard. Our mission focuses on ensuring that creators maintain complete control over their creative processes with AI, offering them innovative tools to transform their ideas into reality.If you have a passion for pushing the limits of AI technology, we would love to have you join our team!

Join Our TeamLatent Labs is on the lookout for a talented Machine Learning Research Engineer with extensive expertise in training and deploying large-scale generative models. In this pivotal role, you will oversee our high-performance computing infrastructure and drive our model serving initiatives. You will collaborate with a diverse team of machine learning experts, protein engineers, and biologists, all dedicated to transforming how we manipulate biological systems and develop innovative treatments for diseases.About Latent LabsAt Latent Labs, we are pioneers in creating advanced models that grasp the core principles of biology. Our team, which once co-developed DeepMind's groundbreaking AlphaFold and invented latent diffusion, is committed to scientific rigor and ambitious exploration. By joining us, you will work alongside some of the most brilliant minds in the fields of generative AI and biology, fostering a culture of collaboration and continuous learning between our London and San Francisco offices.We seek innovators who are passionate about solving complex problems and making a positive impact on the world. Be a part of our ambitious mission!

At Orbital Materials, we leverage advanced AI technologies to engineer data center hardware that surpasses industry standards. Our AI capabilities simulate materials at the atomic level and evaluate millions of hardware configurations within a fraction of the time traditional methods require. This innovative approach leads to hardware with unmatched specifications: 1 MW/rack, PUE Every deployment contributes valuable field data that refines our AI models. Improved models yield superior hardware, and this cycle of continuous enhancement accelerates our advancements in AI. We are not merely observers of AI progress; we actively influence its trajectory.Our focus on data centers is driven by the urgent market demand and stringent specifications, but our AI-driven development process—covering materials discovery, hardware design, and manufacturing optimization—extends to any complex physical system. Data centers serve as our initial testing ground, not our limit.With operations in London, Canada, and the USA, we are expanding our teams across Machine Learning Research, Product Development, Mechanical Engineering, and Chemical Engineering. If you're eager to explore the intersection of AI and materials science, we want to connect with you.In the role of Staff Machine Learning Researcher, you will design and implement state-of-the-art AI systems for multi-scale physical technology design. Our multi-scale approach involves creating leading foundation models that simulate both the atomic motion of materials and the macroscopic fluid dynamics in 1GW data centers. You will facilitate cross-scale co-design, utilizing the expertise of our scientists and engineers, complemented by industry-leading domain agents.This position requires you to set high technical standards and lead projects from initial prototype stages to full production deployment. We seek a candidate who is passionate about craftsmanship, committed to continuous learning, and adept at building scalable systems. A collaborative spirit and a genuine enthusiasm for applying AI to tackle significant global industrial challenges are essential.

Isomorphic Labs is pioneering the application of advanced AI technologies to unlock profound scientific insights, expedite breakthroughs, and develop transformative medicines with a vision to eradicate all diseases.The future is on the horizon, made possible by the remarkable capabilities of machine learning. We envision a world where diseases are effectively managed or eliminated through accelerated and enhanced drug discovery.Join our interdisciplinary team to spearhead groundbreaking innovations and play a pivotal role in our mission to achieve ambitious health-related goals, all while contributing to a vibrant and collaborative workplace culture.The future we aspire to is being shaped today. It begins with our culture at Isomorphic Labs. It starts with you.About Isomorphic LabsFounded in 2021, Isomorphic Labs (IsoLabs) strives to enhance human health by building upon the Nobel Prize-winning AlphaFold system. Our diverse team of experts in drug discovery and machine learning has developed groundbreaking predictive and generative AI models that accelerate scientific discovery at an unprecedented pace.Our name reflects our belief in the inherent symmetry between biology and information science. By leveraging AI's robust capabilities, we can model complex biological processes to design innovative molecules, predict drug performance, and develop novel treatments for some of the world's most challenging diseases.Our state-of-the-art drug design engine integrates AI models capable of addressing various therapeutic areas and drug modalities. We are dedicated to continuously innovating model architectures and creating cutting-edge solutions to advance rational drug design.With each significant advancement, we are progressing towards the promise of digital biology, moving closer to our ambitious mission of ultimately solving all diseases through the power of AI.

Waymo is at the forefront of autonomous driving technology, striving to become the world's most reliable driver. Originating from the Google Self-Driving Car Project in 2009, Waymo has dedicated itself to developing the Waymo Driver—The World’s Most Experienced Driver™. Our goal is to enhance mobility access while preventing the tragic loss of life caused by traffic accidents. The Waymo Driver powers our fully autonomous ride-hailing service and is adaptable across various vehicle platforms and applications. To date, we've completed over ten million rider-only trips, with extensive experience driving autonomously over 100 million miles on public roads and conducting tens of billions of miles in simulation across more than 15 U.S. states.The Simulator team is responsible for creating cutting-edge simulations that mimic realistic environments to train and test the Waymo driver. Through advanced machine learning techniques, we model real-world scenarios, including realistic agents such as vehicles, pedestrians, cyclists, motorcyclists, and more, alongside roads, traffic systems, and weather conditions. By leveraging large foundation models, trained on our vast datasets, we can rapidly set up and deploy diverse scenarios to challenge our driver.We are excited to establish a team in London, UK, to collaborate with our colleagues in Mountain View and Oxford on enhancing these foundational models and integrating them into various training and evaluation tools. We seek enthusiastic research engineers eager to tackle these groundbreaking challenges.In this hybrid role, you will report directly to an Engineering Director.

Join Our Innovative Team as a Machine Learning ResearcherAt Wintermute, we are on the forefront of digital asset trading, leveraging cutting-edge technology to provide unparalleled liquidity and OTC solutions across the cryptocurrency landscape. As a technology unicorn founded in 2017, we are a leading player in algorithmic trading, committed to supporting high-profile blockchain projects and ushering traditional financial institutions into the exciting world of crypto. Our venture arm also invests in early-stage DeFi initiatives, fostering innovation in the blockchain space.Our culture merges the rigor of high-frequency trading with the innovative spirit of tech startups, creating an environment that champions both technological excellence and entrepreneurial ambition. We believe in the transformative potential of blockchain technology, maintaining a long-term vision for the digital asset market while prioritizing compliance and innovation.Your Role and ResponsibilitiesAs a Machine Learning Researcher, you will harness your expertise in applied deep learning to develop robust alpha signal generation pipelines. Your work will encompass everything from data ingestion and feature engineering to model training and deployment, all in close collaboration with our dynamic trading and infrastructure teams.

Role overview The Staff Machine Learning Engineer at AccuRx will work onsite in London (Shoreditch). The main focus is on creating and deploying machine learning models that help improve patient care and make healthcare workflows more efficient. This work aims to benefit both patients and healthcare professionals. What you will do Design, build, and deploy machine learning models for healthcare use cases Collaborate with engineering, product, and clinical teams to shape solutions Contribute to projects that influence patient outcomes and support healthcare providers

We invite candidates to apply for the position that best aligns with their expertise. If we find a better fit for your profile, we will contact you about alternative opportunities. Position Overview: Conduct research utilizing statistical techniques, including time-series analysis, machine learning, and natural language processing to derive insights from data. Analyze extensive data sets employing advanced statistical and machine learning methodologies to uncover trading opportunities. Collaborate in developing statistical and machine learning tools to address complex data-related challenges across the organization. Typical Day: Engage in researching innovative statistical and machine learning techniques and exploring various datasets throughout the day. Present and discuss research findings with fellow researchers. Deploy and oversee models utilized for generating trading signals.

About the Position We are seeking intelligent and inquisitive individuals to enhance our dynamic Machine Learning team at Jane Street. This role offers the opportunity to develop cutting-edge deep learning models that underpin our trading strategies, utilizing our expansive computing cluster equipped with tens of thousands of high-performance GPUs. The trading environment presents unique challenges, including large-scale models and nonstationary datasets within a competitive multi-agent landscape, prompting innovative solutions. At Jane Street, our researchers, engineers, and traders collaborate closely, sharing knowledge and expertise just a few feet apart. Your day may involve analyzing market data, optimizing hyperparameters, diagnosing distributed training performance, or exploring our models' behaviors in live trading scenarios. Your extensive knowledge of the machine learning domain and familiarity with diverse methodologies—ranging from LLMs, image recognition systems, RL agents, recommendation frameworks, to classical ML techniques—will be invaluable in advancing ML at Jane Street. You will play a crucial role in training models for our next-generation deep learning trading strategies and developing the insights necessary to navigate new markets and challenges. Furthermore, you will be involved in the recruitment of new team members, attending industry conferences, and sharing your expertise with colleagues, all of which are considered significant contributions to our mission.

Join latentlabs as a distinguished Machine Learning Researcher specializing in generative modeling. You will collaborate with a diverse team of machine learning experts, protein engineers, and biologists, all dedicated to revolutionizing biological control and disease treatment. Your primary focus will be to design innovative generative models aimed at creating new proteins that demonstrate functionality in wet lab assays.Who You AreExpert in Machine Learning Research. You possess substantial experience in generative modeling and have contributed to significant machine learning projects, as evidenced by your involvement in prominent open-source libraries, impactful product launches, or high-caliber publications at conferences such as NeurIPS, ICML, or ICLR.Skilled ML Developer. You produce robust, well-tested, and maintainable ML code. Your familiarity with version control and code review practices ensures high-quality development. You excel at rapid prototyping while also crafting polished production-ready code, with experience in running training and inference on cloud infrastructure.Data Engineering Proficient. You have a strong background in creating ML data pipelines for training and evaluating deep learning models. You’re skilled at analyzing raw data, creating effective dataset splits, and building scalable pipelines.Passionate About Model Performance. Your deep understanding of ML libraries, hardware interactions, and data optimization enables you to enhance the performance of deep learning models, focusing on training and inference speed and validation metrics.Driven and Inquisitive. You have a strong desire to effect positive change, whether for patients or customers. You adapt flexibly to diverse methodologies and are curious about challenges, no matter their scale. Your ability to thrive in a fast-paced environment allows you to achieve goals effectively.What Sets You ApartExperience in Computational Biology or Protein Design. You have engaged in ML-driven projects within the biological domain.Natural Sciences Background. Your foundation in natural sciences supports your work in machine learning applications.

Waymo, a pioneer in autonomous driving technology, is dedicated to becoming the world’s most trusted driver. Since its inception as the Google Self-Driving Car Project in 2009, Waymo has been at the forefront of innovation, developing the Waymo Driver—The World’s Most Experienced Driver™—to enhance mobility while significantly reducing the number of lives lost in traffic accidents. The Waymo Driver powers our fully autonomous ride-hailing service and is adaptable to various vehicle platforms and applications. With over ten million rider-only trips completed, fueled by our experience of autonomously driving over 100 million miles on public roads and tens of billions of miles in simulation across more than 15 U.S. states, we are transforming transportation.The Driver Understanding and Evaluation (DUE) team at Waymo is focused on creating comprehensive metrics to evaluate the Waymo Driver’s behavior in real-world scenarios. We are developing advanced technologies such as context and scene analysis to enhance our driving understanding, as well as augmenting real-world driving data to generate rare driving events. This includes building large-scale data infrastructures and improving components like agents and realistic simulators, which are pivotal for our technical strategies and methodologies in assessing the Waymo Driver's performance.The DUE Machine Learning team will develop and maintain scalable machine learning systems and simulation workflows, enhancing evaluation processes and developer onboarding experiences. By merging expert human insights with cutting-edge machine learning models, we will provide training and evaluation data across hundreds of metrics and components that define the Waymo Driver. We are seeking passionate researchers and software engineers eager to harness machine learning techniques to elevate the evaluation systems within our autonomous vehicles, driven by a relentless pursuit of performance improvement.This position reports to the Engineering Manager.

Join our dynamic team at Spotify as a Staff Machine Learning Engineer, where you will be at the forefront of developing innovative AI solutions that enhance user experiences. In this role, you'll collaborate with cross-functional teams, leveraging your expertise in machine learning algorithms and data analysis to drive impactful projects.Your responsibilities will include designing and implementing machine learning models, optimizing algorithms for performance, and conducting research to stay ahead of industry trends. You will also mentor junior engineers, fostering a culture of knowledge sharing and continuous improvement.

About H H is focused on advancing superintelligence by developing agentic AI. The team’s mission is to automate complex, multi-step tasks that typically require human effort, enabling AI agents to extend what people can achieve. H values open collaboration, continuous learning, and responsible progress. The company seeks people who care deeply about building agentic technologies safely and who want to contribute to a culture where every voice matters. About the Agent Team The Agent team shapes new learning algorithms and agent paradigms that move agentic systems forward. Work centers on foundational models and reinforcement learning, with a focus on training artificial general agents. Collaboration with the LLM/VLM and Safety teams is central, as the group explores new research directions together. Role Overview: Technical Staff Member - Research - Agent This position is part of the research team and centers on engineering. The main responsibility: define system architectures and build scalable platforms that underpin research efforts. The work involves turning research ideas into reliable, high-performance systems ready for production, supporting the next wave of agentic AI. Location London

At Orbital Industries, we harness the power of artificial intelligence to revolutionize data center hardware, setting new benchmarks in performance. Our innovative AI technology simulates materials at the atomic level and evaluates millions of hardware configurations far more rapidly than traditional methods, allowing us to uncover optimal designs that surpass industry standards.Each deployment generates valuable field data that continuously enhances our AI models. This iterative process leads to superior hardware, which in turn fuels more sophisticated AI capabilities. Our development cycle is a closed loop that tightens with every iteration, making us not just participants in AI evolution, but powerful catalysts for its acceleration.We focus on data centers as our initial market, given the urgent specifications and competitive landscape. However, our AI-driven methodologies—spanning materials discovery, hardware design, and manufacturing optimization—are versatile enough to apply to any intricate physical system. Data centers serve as our proof of concept, not our limiting factor.With teams located in London, Canada, and the USA, we're expanding our capabilities in machine learning research, product development, mechanical engineering, and chemical engineering. If you are eager to explore the intersection of AI and atomic science, we would love to connect with you.As a Senior Machine Learning Researcher at Orbital Industries, you will be responsible for designing cutting-edge AI systems for the multi-scale design of physical technologies. Our work encompasses creating world-class foundational models that simulate both atomic motion and liquid flow in 1GW data centers. You will collaborate across these different scales, leveraging the expertise of our scientists and engineers, enhanced by top-tier domain agents.In this role, you will establish high technical standards and guide projects from their inception to production deployment. We seek an individual who is passionate about craftsmanship, committed to continuous learning, and dedicated to developing scalable systems. A low-ego approach and a genuine enthusiasm for employing AI to tackle significant global industrial challenges are essential qualities we value.

About EDITEDEDITED is the premier AI-driven retail intelligence platform globally, empowering the most successful brands and retailers with the ability to make real-time decisions.By seamlessly connecting internal business data with external market insights, EDITED integrates intelligence into every retail decision. Our solutions help retailers enhance their margins, boost sales, and achieve superior business outcomes through AI-powered market and enterprise intelligence that propels automation.At EDITED, we cultivate a vibrant and inclusive culture where creativity flourishes, and collaboration is fundamental to our success. Our dynamic and supportive environment encourages team members to take initiative, innovate, and grow continuously. We prioritize diversity, transparency, and a shared commitment to excellence, fostering a workplace where every voice is valued, and contributions are celebrated.We understand that a healthy work-life balance is essential for driving innovation and success. Our flexible working arrangements—including hybrid working, flexible hours, and a work-from-anywhere policy—enable our team to perform at their best.