Staff Machine Learning Software Engineer - Research

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.

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.

Waymo is a pioneering company in the field of autonomous driving technology, dedicated to becoming the most trusted driver in the world. Originating from the Google Self-Driving Car Project in 2009, Waymo has concentrated on developing the Waymo Driver—The World’s Most Experienced Driver™—to enhance mobility access and save lives lost to traffic accidents. The Waymo Driver supports our fully autonomous ride-hailing service and is adaptable across various vehicle platforms and applications. With over ten million rider-only trips and more than 100 million miles driven autonomously on public roads, complemented by tens of billions of miles in simulation across 15+ U.S. states, our experience is unparalleled.The Simulator team is at the forefront of creating cutting-edge simulations of real-world environments for the training and assessment of the Waymo Driver. We leverage machine learning to accurately model the real world, incorporating realistic agents (vehicles, pedestrians, cyclists, motorcyclists, etc.), road systems, traffic controls, and weather conditions. To enhance the precision and flexibility of our simulations, we utilize large foundation models trained on extensive datasets, enabling rapid setup and deployment of multiple scenarios for our driver.Our London-based team collaborates closely with our colleagues in Mountain View and Oxford to develop and enhance these foundation models and integrate them into various evaluation and training products. We are seeking talented research engineers eager to tackle these innovative challenges.In this hybrid role, you will report directly to an Engineering Manager.

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:

Waymo is at the forefront of autonomous driving technology, dedicated to becoming the world's most trusted driver. Originally initiated as the Google Self-Driving Car Project in 2009, our team has been relentlessly working to enhance mobility access and save lives by developing the Waymo Driver—The World’s Most Experienced Driver™. With over ten million successful rider-only trips facilitated by our technology, which has driven over 100 million miles on public roads and tens of billions of miles in simulations across more than 15 U.S. states, we are shaping the future of transportation.The Simulation ML Infrastructure team is responsible for creating scalable AI/ML infrastructure that propels our Simulator team towards innovative, state-of-the-art simulations of realistic environments for the Waymo Driver's training and testing. By leveraging large foundation models trained on extensive datasets, we enhance the authenticity and control of our simulations, including realistic representations of vehicles, pedestrians, cyclists, roads, traffic control systems, and weather conditions.We are currently seeking a highly skilled Senior Machine Learning Infrastructure Engineer to spearhead the development of advanced AI/ML infrastructure for multi-billion parameter foundation models tailored for ML accelerator-friendly simulations. Your extensive knowledge in massive model scaling, ML accelerators, and distributed training will be vital in designing and optimizing our systems.

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

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!

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 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.

At Orbital Materials, we harness the power of AI to revolutionize data center hardware, ensuring our products consistently outperform the competition. Our advanced AI simulates materials at the atomic level, allowing us to evaluate millions of hardware configurations in the time it would typically take to assess just hundreds. This innovative approach enables us to discover optimal designs that surpass traditional benchmarks, resulting in hardware specifications that competitors cannot match: 1 MW/rack, PUE As each deployment generates valuable field data, our AI models become increasingly sophisticated. This creates a virtuous cycle where improved models lead to better hardware, which in turn enhances our AI capabilities. We are not merely benefitting from AI advancements; we are actively driving its pace.Our focus on data centers is driven by an urgent market need and demanding specifications. However, the AI-accelerated development processes we've established—spanning materials discovery, hardware design, and manufacturing optimization—are applicable to any complex physical system. Data centers serve as our initial proof point, not the limit of our ambitions.With operations in London, Canada, and the USA, we are assembling teams across various domains including ML research, product development, mechanical engineering, and chemical engineering. If you're eager to work at the intersection of AI and physical sciences, we'd love to hear from you.As a Staff Machine Learning Engineer at Orbital, you will be instrumental in architecting sophisticated AI systems for the multi-scale design of physical technologies. By multi-scale, we mean that we create world-class foundational models capable of simulating everything from the microscopic motion of atoms to the macroscopic flow of liquids in 1GW data centers. You will collaborate across these diverse scales, leveraging the creativity of our scientists and engineers, augmented by premier domain agents.In this pivotal role, you will establish exceptionally high technical standards and lead projects from initial prototypes to full-scale production deployment. We seek individuals who possess a passion for craftsmanship, a commitment to continual learning, and a drive to build scalable systems. Additionally, we value a low-ego mindset and a genuine enthusiasm for leveraging AI to tackle significant global industrial technology challenges.

About UsFounded in the United States in 2022 and now headquartered in London, UK, Recraft is revolutionizing the creative landscape with its cutting-edge AI tool designed specifically for professional designers, illustrators, and marketers. Our platform sets a new benchmark in image generation excellence.Our innovative tool empowers creators to swiftly generate and refine original images, vector art, illustrations, icons, and 3D graphics using AI technology. With over 3 million users across 200 countries, who have collectively produced hundreds of millions of images, Recraft is just at the beginning of its journey.Join us and explore a universe of professional opportunities! Contribute to large-scale projects and help shape the future of creativity. We are dedicated to making Recraft an indispensable daily tool for every designer, setting the industry standard. Our mission is to enable creators to fully control their creative process with AI, equipping them with innovative tools to turn their ideas into reality.If you are enthusiastic about pushing the boundaries of AI, we welcome you to join our team!

Join datatonic as a Machine Learning Engineer and be part of a dynamic team dedicated to leveraging data to drive intelligent decision-making. You will design and implement machine learning models, optimize algorithms, and collaborate with cross-functional teams to deliver innovative solutions.

Join datatonic as a Senior Machine Learning Engineer and be a pivotal part of our innovative team focused on transforming data into actionable insights. In this role, you will leverage your expertise in machine learning to develop and enhance algorithms, contribute to data-driven solutions, and collaborate with cross-functional teams to push the boundaries of data analytics.

Join Trainline as the Head of Machine Learning Engineering, where you'll lead our innovative machine learning initiatives. In this pivotal role, you will spearhead the development and implementation of advanced ML solutions that enhance our customer experience and optimize our operations.

Join Faculty as a Machine Learning Engineer and be at the forefront of innovation in artificial intelligence. In this role, you will leverage your expertise in machine learning to develop and implement cutting-edge algorithms and models that drive impactful solutions across various industries.As a part of our dynamic team, you will work collaboratively to transform complex data sets into actionable insights, optimizing our products and enhancing client offerings. If you are passionate about technology and eager to make a difference, we would love to hear from you!

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.

Waymo is an innovative leader in autonomous driving technology, striving to be the most trusted driver globally. Originating from the Google Self-Driving Car Project in 2009, our focus has been on developing the Waymo Driver—The World's Most Experienced Driver™—to enhance mobility access while significantly reducing traffic-related fatalities. The Waymo Driver underpins our fully autonomous ride-hail service and is versatile enough to integrate with various vehicle platforms and applications. With over ten million rider-only trips facilitated and a flawless record of driving over 100 million miles on public roads and tens of billions in simulation across more than 15 U.S. states, our mission is clear.The DUE ML Core London team is responsible for designing and maintaining robust machine learning systems, simulation workflows, and analytics tools aimed at enhancing evaluation processes and developer onboarding experiences. By merging expert human judgment with state-of-the-art machine learning models, we provide training and evaluation data for a multitude of metrics and components integral to the Waymo Driver.We are seeking passionate researchers and software engineers who are enthusiastic about advancing machine learning techniques for evaluation systems and driving performance enhancements throughout our technology stack.

Join Almedia as a Machine Learning Engineer, where you'll be at the forefront of innovation in artificial intelligence and machine learning technologies. Your role will involve designing and implementing machine learning models to solve complex real-world problems. Collaborate with a talented team of engineers and data scientists to enhance our products and services through cutting-edge algorithms and data-driven solutions.

Intercom is an innovative AI Customer Service company dedicated to empowering businesses to deliver exceptional customer experiences.Our state-of-the-art AI agent, Fin, is recognized as the leading customer service AI solution available. It enables businesses to provide consistent, high-quality customer service, thereby enhancing their overall customer experience. Fin can seamlessly integrate with our Helpdesk to form the comprehensive Intercom Customer Service Suite, which offers AI-enhanced support for more complex queries that necessitate human intervention.Founded in 2011 and trusted by nearly 30,000 businesses worldwide, Intercom is redefining the customer service landscape. Guided by our core values, we continuously challenge the status quo, act with urgency, and strive to deliver remarkable value to our clients.What is the Opportunity?Our Machine Learning team at Intercom is pivotal in identifying and developing new ML features, exploring suitable algorithms and technologies, and swiftly delivering initial prototypes to our customers.We are a highly product-focused team, collaborating closely with our Product and Design partners. The dedicated ML product engineers on our team enable us to expedite our transition to production, often launching beta versions just weeks after successful offline testing.We are deeply passionate about harnessing machine learning technology, having productized a range from traditional supervised models to state-of-the-art unsupervised clustering algorithms and innovative applications of transformer neural networks. We rigorously test and evaluate the tangible customer impact of each model we deploy.What Will I Be Doing?Actively participate in recruitment, mentoring, and career development initiatives for fellow engineers.Elevate technical standards, performance metrics, reliability, and operational excellence.Pinpoint opportunities where ML can deliver significant value to our customers.Collaborate with team members and Product and Design stakeholders to define the appropriate ML framework for product challenges.Conduct exploratory data analysis and research to gain a deep understanding of the problem domain.Investigate and select suitable algorithms and tools, balancing practicality with cutting-edge innovation where necessary.Execute offline evaluations to validate algorithm efficacy.Collaborate with engineers to transition prototypes to production.Plan, measure, and disseminate insights to guide iterative improvements.