We're building out a trading initiative in London, with a growing focus on listed commodities options. This expansion is supported by a mix of internal talent and experienced external hires. With a collaborative and entrepreneurial culture, the team offers a unique opportunity to help shape and scale a new desk from an early stage.

The group combines deep expertise with broad trading experience and values innovation, ownership, and cross-functional collaboration. As the desk continues to grow, there will be increasing opportunities to take on broader responsibilities and contribute meaningfully to its success.

Your Core Responsibilities:

• Own fundamental and news analysis, delivering morning market updates, weekly wraps, and previews of key upcoming catalysts for energy markets.
• Work directly with traders and quants to identify and capitalize on short-term dislocations and longer-term macro opportunities in energy markets.
• Develop and refine data-driven models to forecast supply, demand, price, and volatility movements.
• Deliver real-time market insights on geopolitical risks, inventory trends, refinery margins, and systematic flows.
• Work directly with traders and quant teams to translate fundamental views into actionable trading strategies.
• Build and maintain high-quality datasets to enhance market understanding and improve decision-making.
• Take ownership and initiative to drive research and trading strategy development.

Your Skills and Experience:

• Education in Economics, Finance, or a related Engineering field
• 3+ years of experience in energy markets and macro analysis, trading or research.
• Familiarity with options pricing models and volatility dynamics
• Strong understanding of physical and derivatives markets, including supply chains, refinery economics, and hedging flows.
• Entrepreneurial mindset with a passion for market structure, trading flows, and price discovery.
• Proficient in Python, R, or SQL for data analysis and model development.
• Ability to communicate complex insights clearly and concisely in a fast-paced trading environment.

IMC is looking for experienced quantitative researchers to develop high to mid frequency delta one trading strategies and predictive models for Equities markets. If you’re excited about helping to push the boundaries of what we can do with Machine Learning in trading, unlocking the significant edges we have in execution, and collaborating to become the best trading firm worldwide, this may be the role for you.

We have longstanding and significant edges across market access, global reach, Options understanding and low latency. The rapid growth we’ve already seen in Machine Learning has unlocked these edges, and some of the most interesting and impactful problems are now being tackled.

IMC competes and wins as a team, with open idea sharing and collaboration across disciplines, desks and offices.

Your Core Responsibilities:

• Conduct large-scale data analysis to generate statistically robust predictions of market behavior, which guide all trading decisions and drive high-impact improvements across global offices.
• Work as part of an established and expanding research team, collaborating closely with traders, software and hardware developers globally to enhance our models and drive measurable improvements in production performance.
• Contribute to defining the strategic direction of research and tooling initiatives.
• Leverage creativity and expertise to quickly produce high-quality, testable ideas.
• Apply a disciplined and systematic approach to ensure results are robust and thoroughly validated.
• Build a deep understanding of market dynamics and potential optimization areas, working closely with operational traders to implement improvements.

Your Skills and Experience:

• Graduate & Postgraduate studies from a leading University; majoring in Machine Learning, Statistics, or STEM related subjects.
• 3+ years’ experience as a Quantitative Researcher, with specific experience in the high to mid-frequency delta one space, ideally within Equities markets.
• A proven track record of developing value-adding and orthogonal features across multiple families.
• Practical experience extracting value from a range of sources (both public feed and alternative data).
• Strong programming skills in at least one language (python preferred).
• Solid understanding of statistics.
• Significant practical experience with at least one mainstream ML approach is required, whilst familiarity with a variety of machine learning approaches and the risks of overfitting is preferred.
• Exceptional interpersonal and communication skills, combined with a proven capacity to collaborate across disciplines and work autonomously to achieve goals.

We're building out a trading initiative in London, with a growing focus on listed commodities options. This expansion is supported by a mix of internal talent and experienced external hires. With a collaborative and entrepreneurial culture, the team offers a unique opportunity to help shape and scale a new desk from an early stage.

The group combines deep expertise with broad trading experience and values innovation, ownership, and cross-functional collaboration. As the desk continues to grow, there will be increasing opportunities to take on broader responsibilities and contribute meaningfully to its success.

 Your Core Responsibilities

• Manage broker relationships in the Commodities team
  Be able to take lots of responsibility and ownership early in your time here to drive the strategy forward.
• Closely collaborating and effectively communicate with Vol and Screen traders to manage risk portfolios
• Leverage an entrepreneurial mindset to identify and research new trading opportunities
• Perform trades analysis to improve trading

Your skills and experience:

• 2+ years of experience as an Execution/Broker Trader
• BS, MS preferably in business, economics or STEM
• You have a quantitative and strategic mindset and a healthy appetite for risk
• Strong technical skills in Statistics and Python
• You have a proven ability to act and make decisions in a fast-paced and competitive environment
• You are analytical, passionate about reflecting on past trades, and always seeking to do better
• Strong communication and teamwork abilities

We're building out a trading initiative in London, with a growing focus on listed commodities options. This expansion is supported by a mix of internal talent and experienced external hires. With a collaborative and entrepreneurial culture, the team offers a unique opportunity to help shape and scale a new desk from an early stage.

The group combines deep expertise with broad trading experience and values innovation, ownership, and cross-functional collaboration. As the desk continues to grow, there will be increasing opportunities to take on broader responsibilities and contribute meaningfully to its success.

 Your Core Responsibilities

• Maximize revenue by identifying and monetizing short- and long-term mispricing’s, as well as managing the resulting positions.
• Be able to take lots of responsibility and ownership early in your time here to drive the strategy forward.
• Managing risk and developing market making strategies
• You will have lots of responsibility and ownership early on to drive strategies forward and be successful
• IMC is highly collaborative between traders, quants, and developers so the scope of responsibility is fairly fluid involving a mixture of data-driven analysis, quantitative modelling and algorithm development

Your skills and experience:

• 3+ years’ experience in Commodities markets
• BS, MS, or PhD, preferably in STEM
• You have a quantitative and strategic mindset and a healthy appetite for risk
• Strong technical skills in Statistics and Python
• You have a proven ability to act and make decisions in a fast-paced and competitive environment
• You are analytical, passionate about reflecting on past trades, and always seeking to do better
• Experience with managing risk and developing market making strategies
• Product experience is preferred
• Strong communication and teamwork abilities

At IMC, we believe technology is the foundation of our competitive edge — and machine learning is increasingly central to how we trade. Over the past few years, we've been steadily building our machine learning capabilities: developing infrastructure, growing our in-house GPU cluster, deploying models into production, and partnering closely with quant researchers and traders to generate real impact. Now we’re expanding the team, scaling our systems, and accelerating the application of deep learning in our research and execution workflows.  We're looking for a Principal Machine Learning Engineer to help shape the next phase of our platform — influencing architecture, driving best practices, and solving high-leverage problems. You’ll work alongside researchers and technologists to design the systems that power experimentation, training, and deployment of ML models — and help set the direction for how machine learning is done at IMC as we scale. If you’ve built ML infrastructure at scale elsewhere and are looking for a role where your ideas will genuinely help shape our firm’s future — we’d love to hear from you.

Your Core Responsibilities: 

• Design and build end-to-end infrastructure for training, evaluation, and productionization of ML models, working closely with our HPC engineers who manage our on-prem compute cluster
• Influence foundational choices around data access, compute orchestration, experiment tracking, model versioning, and deployment pipelines
• Partner with quant researchers to accelerate iteration cycles, tighten feedback loops, and bring models from prototype to live trading
• Work with researchers to adapt and deploy modern architectures — transformers, state-space models, temporal convolutions, graph neural networks — to noisy, high-frequency financial data. Explore techniques like self-supervised pretraining, representation learning, and cross-sectional modelling where they offer genuine edge
• Shape our approach to reproducibility, continual learning, and production monitoring across a petabyte-scale data environment
• Define standards that create consistency across teams and geographies; mentor engineers and influence technical culture beyond your immediate work
• Keep pace with developments in deep learning research and ML infrastructure; bring ideas from academia and industry into how we work — whether that's new architectures, training techniques, or tooling

Your Skills and Experience: 

• 8+ years of experience building ML platforms or infrastructure at a leading tech company, research lab, or quantitative firm
• A track record of designing and owning large-scale training and inference systems — not just contributing, but architecting
• Deep proficiency in Python, with strong experience in either CUDA or C++
• Hands-on expertise with modern deep learning frameworks (PyTorch, TensorFlow, or JAX) and practical experience implementing architectures like transformers, attention mechanisms, or sequence models
• Strong foundation in deep learning fundamentals: optimization, regularization, loss design, and the trade-offs that matter when training at scale
• Experience with distributed training at scale (Horovod, NCCL) and GPU optimization (cuDNN, TensorRT)
• History of deploying models to production with strong observability, reproducibility, and monitoring practices
• Comfort working across the ML stack from data pipelines to training infrastructure to serving systems

Why This Role: 

• Build, don't inherit — You'll make foundational technology choices in a platform that's still being defined, not maintain someone else's legacy.
• Real investment, real backing — This is a strategic priority with resources behind it, not a side experiment.
• Direct impact on trading — Your infrastructure will power models that make real trading decisions in competitive global markets.
• Global scope — Work with teams across New York, Chicago, Amsterdam, London, Sydney, Hong Kong and beyond; define practices that can scale worldwide.
• Ideas over titles — IMC's culture values clarity, rigor, and collaboration. The best ideas win, regardless of where they come from.
• Tight coupling with research — You won't be building in isolation. Researchers and engineers work side-by-side, iterating together.

#LI-DNP

Are you ready to delve into the world of high frequency trading? Do you want to learn more about the role of advanced technology within trading at IMC? Participate in our UK Masterclass in London. The programme will take place on September 27 for Trading and September 28 for Technology.

During this Masterclass, you will discover more about the world of technology driven high frequency trading at IMC. Experience how it is like to be working on the edge between trading and software engineering. You will get to know everything about the basics of interacting with a financial exchange and how to write a strategy that is able to handle market data and calculate profitability. You will explore all of this during not 1, but 2 home-built coding challenges!

Who are you?

• Participants must be enrolled at a university in the UK or Ireland
• Please be aware that while we understand some universities may still be on summer break, we are only able to offer flight reimbursement for participants travelling from within Europe
• Unfortunately, we are unable to assist with visa applications for entry into the UK
• Studying a relevant degree (e.g. Computer Science, Software Engineering or a related field of study)
• Graduating in 2027 or 2028

What can you expect?

• Lectures to learn more about the world of high frequency trading and technology at IMC
• Algorithmic optimisation coding challenge, in your preferred language
• Q&A’s to meet with our Software Engineers and learn more about working at IMC
• Free lunch, drinks and snacks

The event will be held in London from 10:00 until 18:00. We expect you to travel to the location yourself (travel costs will be reimbursed). 

Excited to join our UK Masterclass? Apply for our Expression of Interest so you are the first one to hear about it when we go live!

As a Machine Learning Engineer, you will play a pivotal role in building systems that drive the training and deployment of large-scale ML models across our global operations. You'll collaborate with leading researchers, hardware experts, and software engineers to build robust solutions that maximize the potential of GPU acceleration, distributed computing, and the latest open-source tools. Your work will influence our trading strategies by accelerating experimentation cycles that foster continuous innovation and refinement.

This is a unique opportunity to solve problems at the intersection of advanced machine learning and trading, where your contributions will shape the future of IMC’s technology and trading capabilities.

Your Core Responsibilities: 

• Develop large-scale distributed training pipelines to manage datasets and complex models
• Build and optimize low-latency inference pipelines, ensuring models deliver real-time predictions in production systems
• Develop libraries to improve the performance of machine learning frameworks
• Maximize performance in training and inference using GPU hardware and acceleration libraries
• Design scalable model frameworks capable of handling high-volume trading data and delivering real-time, high-accuracy predictions
• Collaborate with quantitative researchers to automate ML experiments, hyperparameter tuning, and model retraining
• Partner with HPC specialists to optimize workflows, improve training speed, and reduce costs
• Evaluate and roll out third-party tools to enhance model development, training, and inference capabilities
• Dig into the internals of open-source ML tools to extend their capabilities and improve performance

Your Skills and Experience: 

• 5+ years of experience in machine learning with a focus on training or inference systems
• Hands-on experience with real-time, low-latency ML pipelines in high-performance environments is a strong plus
• Strong engineering skills, including Python, CUDA, or C++
• Knowledge of machine learning frameworks such as PyTorch, TensorFlow, or JAX
• Proficiency in GPU programming for training and inference acceleration (e.g., CuDNN, TensorRT)
• Experience with distributed training for scaling ML workloads (e.g., Horovod, NCCL)
• Exposure to cloud platforms and orchestration tools
• A track record of contributing to open-source projects in machine learning, data science, or distributed systems is a plus

#LI-DNP