Graduate Machine Learning Researcher at Longshot Systems | London

Join Longshot Systems, where we are pioneering cutting-edge platforms for sports betting analytics and trading.We are looking for enthusiastic Graduate Machine Learning Researchers to become integral members of our quantitative modeling team. This team is dedicated to enhancing the predictive capabilities of our models using historical event data, as the excellence of our models is crucial to our company’s success.In this role, you will be responsible for designing, testing, and implementing innovative machine learning models in Python while continuously refining our existing top-tier solutions. As a small, focused company, Longshot offers you the opportunity to engage deeply in all facets of the R&D process, from high-level design to production implementation, while also learning from experienced industry professionals.The ideal candidate will possess a creative mindset and thrive in generating novel approaches to problem-solving. You will have the autonomy to explore and research the most suitable methods for the challenges presented. A solid mathematical foundation in Machine Learning principles and core statistics is essential, though prior knowledge of sports betting is not a requirement.We embrace a hybrid working model, with in-office work on Thursdays at our London (Farringdon) location and remote work for the remainder of the week. Our regular working hours are from 10 AM to 6 PM UK time, Monday to Friday, with a strong emphasis on flexible working to empower our team to achieve their objectives.Our interview process consists of the following steps:Introductory call (30 mins) - Discuss your background and interestsTechnical interview (60 mins) - Engage in modeling discussions and scenario-based questionsFull assessment day (9:30 AM–5 PM) - Tackle a real modeling challenge utilizing near-production dataQualifications A PhD or research Master’s degree in a quantitative, technical discipline (e.g., Mathematics, Physics, Machine Learning) from a reputable university Proficiency in modeling tabular data using Python Benefits Participation in an uncapped company bonus scheme, typically ranging from 10-20% of salary based on experience 10% matched pension contributions Private healthcare coverage Long-term illness insurance Gym membership Choice of hardware and setup for your development environment

Join Longshot Systems, where we are at the forefront of developing sophisticated platforms for sports betting analytics and trading.We are in search of talented Machine Learning Researchers to join our quantitative modeling team. This team’s primary objective is to enhance the predictive capabilities of our models using historical event and market data. The quality of our models is paramount, as improvements directly influence our company’s success.In this role, you will design, test, and implement innovative machine learning models using Python, continuously enhancing our existing state-of-the-art solutions. As a small, focused company, we offer you the chance to be involved in every aspect of the R&D process, from high-level design to production implementation.The ideal candidate will possess high creativity and enjoy developing new, innovative approaches to problem-solving, and will have the autonomy to explore the most suitable methods for the challenges at hand. A strong mathematical foundation in machine learning and core statistics is essential. While knowledge of sports betting is not required, experience with modeling sports — particularly in-play football, basketball, or tennis — is advantageous.We embrace a hybrid working model, requiring in-office presence on Thursdays at our London (Farringdon) office, while allowing flexibility for the remainder of the week. Our standard working hours are from 10 am to 6 pm UK time, Monday to Friday, with support for flexible schedules to help our team achieve their goals.Our interview process includes:Introductory call (30 mins) - discussing your background and interestsTechnical interview (60 mins) - focusing on modeling questions and a coding exerciseFull assessment day (10:30 am – 5 pm) - encompassing a comprehensive modeling exercise and team interactions

Longshot Systems builds advanced platforms for sports betting analytics and trading. The team blends software engineering, machine learning, and expertise in sports markets to develop trading strategies for clients. Role overview The Sports Betting Trading Analyst joins the modeling team as a subject matter expert in sports and betting markets. This role works closely with software engineers and machine learning specialists to refine and improve trading strategies. Key responsibilities include monitoring client trading activity, analyzing historical trade data, and applying deep knowledge of the sports betting landscape to spot improvements and value leaks in both current and new strategies. What you will do Work with engineers and machine learning specialists to enhance trading strategies Analyze historical trades and client activity to identify areas for improvement Apply expertise in sports and betting markets to find value opportunities and leaks Use data analysis tools, especially Python and Jupyter notebooks, to extract insights Engage with a wide range of sports from a betting perspective Requirements Strong passion for sports and sports betting, ideally with experience as a professional bettor Deep understanding of betting exchanges, prediction markets, and sharp bookmakers, including US and Asian markets Proficiency with Jupyter notebooks and data analysis in Python Strong numerical skills and a solid background in statistics Interest in following a broad range of sports, especially through the lens of betting Working arrangements This position follows a hybrid working model. Thursdays require in-person attendance at the London (Farringdon) office, while the rest of the week offers flexibility. Core hours are 10 am to 6 pm UK time, Monday through Friday, with support for flexible working to help the team meet goals. The role often benefits from engagement during sporting events, and tools are available to review games outside regular hours. Interview process Introductory call (30 minutes) to discuss background and interests First technical interview (90 minutes) for an in-depth discussion of experience Comprehensive assessment day (10:00 am - 6:00 pm) on-site, focused on data analysis using Python to generate insights

At Longshot Systems, we develop cutting-edge platforms for sports betting analytics and trading.We are currently seeking experienced Machine Learning Engineers to join our modeling engineering team. In this role, you will collaborate closely with our quantitative research teams to transform prototype trading models into robust, production-ready systems. You will be responsible for designing and building the tooling, frameworks, and data engineering necessary to support strategy research and development, while also architecting high-level designs of the strategy software to minimize trading latency and ensure scalability. Our ML stack is primarily Python-based, incorporating modern ML libraries and tools such as Polars, Ray, and Plotly.The ideal candidate will possess a solid software engineering background, with extensive experience in high-performance computing topics such as multi-threading, networking, profiling, and optimization. Proficiency with the NumPy/SciPy stack is essential, along with experience in performance optimization tools like C++ and Numba. Familiarity with common ML algorithms and techniques is advantageous but not mandatory.As a hybrid working company, we require team members to work in our London (Farringdon) office on Thursdays, while offering flexibility for remote work on other days. Our standard working hours are from 10 am to 6 pm UK time, Monday to Friday, but we encourage flexible schedules to help our team meet their objectives.Interview Process:Introductory Call (30 mins) - Discuss your background and interestsFirst Technical Interview (30 mins) - Live code review and pair programmingSecond Technical Interview (60 mins) - In-depth technical questionsFull Assessment Day (10:30 am to 5 pm) - A programming exercise reflective of actual team tasks

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.

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.

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.

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 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 Longshot Systems Ltd, where we create cutting-edge platforms for sports betting, analytics, and trading. We are seeking a Senior DevOps Engineer to enhance our platform team.You will manage a medium-sized deployment that connects with global sports betting platforms and supports multiple internal teams with diverse infrastructure needs. Your role will involve overseeing critical systems from backtesting to real-time trading and long-running backend services.Our infrastructure is hosted on AWS, and we are undergoing an evolution focused on automation and repeatability to drive our growth. Our core systems process thousands of trading signals per second, requiring minimal latency, presenting similar challenges to high-frequency trading environments but in the sports betting sector.As a Senior DevOps Engineer, you will collaborate closely with the CTO and development team to maintain our production and development trading infrastructure. You will take the lead on developing new infrastructure and troubleshooting performance and stability issues across applications, networks, and operating systems. You should be adept at analyzing metrics, packet dumps, and logs to identify and resolve issues, followed by implementing automation to prevent future occurrences.The ideal candidate is autonomous and innovative, thriving on generating new solutions and suggesting improvements to current methodologies. We offer you flexibility and the freedom to explore effective solutions.We embrace a hybrid work model, requiring in-office attendance on Thursdays at our London (Farringdon) office, with flexible arrangements for the remainder of the week. Our standard working hours are 10 AM to 6 PM UK time, Monday to Friday, with support for flexible schedules to achieve your goals.Regarding on-call responsibilities, while we do not maintain a 24/7 rota, we have a compensated on-call system based on best efforts, which will involve sharing responsibilities within the platform team.Interview Process:Introductory call (30 mins) – discuss your background and interests.First technical interview (30 mins) – live systems management and code review.Second technical interview (60 mins) – in-depth technical questions.Full assessment day (10:30 AM – 5 PM) – a practical systems engineering exercise reflecting actual team work. Meet the team, tour the office, and enjoy a complimentary lunch.

Join Longshot Systems, a pioneering company dedicated to developing cutting-edge platforms for sports betting analytics and trading.We are in search of a Senior Software Engineer to become a vital member of our trading platform team. As a small but highly focused organization, we seek an individual who can effectively architect and implement solutions to achieve our upcoming product, performance, and stability objectives. This role involves ownership of our technical standards, coding guidelines, CI/CD processes, observability, and security measures. If you are passionate about delivering features and enhancing the overall technical excellence of our team and platform, we want to hear from you.Our trading platform is responsible for integrating with numerous bookmakers and exchanges globally, normalizing and consolidating global odds data across various sports for internal trading strategies. We manage thousands of odds updates and trades per second, maintaining stringent standards for latency, correctness, and feed quality. You will be at the forefront, integrating additional trading venues, optimizing key platform components, and enhancing the safety and speed of trading. Our team also oversees a large historical data repository, supporting the company's broader trading and research initiatives.Our key platform systems are primarily built using Golang, featuring message buses and databases at the core of our architecture. While fluency in Golang is advantageous, it's not a prerequisite, as we have a team full of experts ready to assist you in learning.We embrace a hybrid work model, with Thursdays in our London office (Farringdon) and flexible arrangements for the rest of the week. Our standard working hours are 10 AM to 6 PM UK time, Monday through Friday, with support for flexible schedules to help our team achieve their objectives.

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!

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.

ASOS seeks a Machine Learning Engineer in London to focus on recommendation systems. The main responsibility involves developing and refining models that deliver personalized product suggestions to customers. Key responsibilities Design and implement machine learning models that improve the relevance of product recommendations. Translate data insights into practical updates for recommendation algorithms, working alongside team members. Contribute to projects aimed at enhancing the user experience through smarter, more tailored suggestions. Collaboration This role involves partnering with colleagues in data science and engineering to share expertise and strengthen the performance of ASOS’s recommendation systems.

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.

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:

Join Jane Street as a Senior Weather Analyst and Machine Learning Researcher, where you will leverage advanced analytical techniques and cutting-edge machine learning algorithms to interpret and predict weather patterns. Your expertise will contribute to our innovative approach to trading strategies and risk management based on meteorological data.

About SwapSwap serves as the backbone of contemporary agentic commerce, being the only AI-native platform that seamlessly integrates backend operations with an innovative storefront experience.Designed for brands looking to sell anything, anywhere, Swap centralizes global operations, enhances intelligent workflows, and empowers margin-protecting decisions through real-time data and capabilities. Our expansive product range covers cross-border transactions, tax solutions, returns, demand planning, and our state-of-the-art agentic storefront, providing merchants with complete transparency to act confidently.At Swap, we cultivate a culture that prioritizes clarity, creativity, and shared ownership as we transform the landscape of global commerce.About the RoleAs the Lead Machine Learning Engineer focusing on Recommendations, you will be responsible for shaping the intelligence behind the AI Storefront at Swap, determining what products are displayed to each shopper. This position is highly technical and hands-on, merging expertise in recommendation systems, large language models (LLMs), and fashion understanding. You will develop the models and pipelines that drive style-aware product recommendations, outfit generation, and personalized discovery, managing the entire process from research and prototyping to production systems that serve actual customers. Collaboration with our conversational AI layer will be essential, as you will extract insightful preference signals through dialogue and integrate these with traditional e-commerce behavioral data and LLM-based world knowledge to enhance recommendations, particularly addressing cold-start challenges in innovative ways.You will establish high technical standards for ML engineering in the recommendations domain at Swap, playing a pivotal role in the evolution of this team area as we scale.

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 intellectually curious and innovative individuals from both industry and academia to join our expanding Machine Learning team. In this role, you will develop deep learning models that enhance our trading strategies, leveraging our advanced computing cluster equipped with tens of thousands of high-performance GPUs. The unique challenges of trading, such as managing large models and dynamic datasets in a competitive multi-agent environment, will inspire you to explore and implement novel solutions. Our collaborative environment brings together researchers, engineers, and traders to work side by side, focusing on training models, designing systems, and executing trading strategies. Each day may involve analyzing market data, optimizing hyperparameters, troubleshooting distributed training performance, or examining how our models perform in live trading. Your expertise in the machine learning realm, including familiarity with various methodologies derived from LLMs, image models, reinforcement learning agents, recommendation systems, and traditional ML approaches, will be crucial in steering the future of ML at Jane Street. You will train models to support the next generation of our deep learning trading strategies and contribute to our foundational knowledge for navigating new markets and scenarios. Additionally, you will play a key role in recruiting new talent, attending industry conferences, and sharing your insights with teammates, all of which are vital aspects of this position.