At a Glance
- Tasks: Design and build ML infrastructure, collaborating with researchers to enhance trading models.
- Company: IMC, a global trading firm with a focus on cutting-edge technology and collaboration.
- Benefits: Competitive salary, bonuses, paid leave, and comprehensive insurance.
- Other info: Dynamic, collaborative environment with global teams and opportunities for innovation.
- Why this job: Shape the future of trading technology and make a real impact in global markets.
- Qualifications: 8+ years in ML platforms, strong Python skills, and experience with deep learning frameworks.
The predicted salary is between 160000 - 200000 £ per year.
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. 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.
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.
The Base Salary range for the role is included below. Base salary is only one component of total compensation; all full-time, permanent positions are eligible for a discretionary bonus and benefits, including paid leave and insurance.
Salary Range: $200,000 - $250,000 USD
About Us:
IMC is a global trading firm powered by a cutting-edge research environment and a world-class technology backbone. Since 1989, we’ve been a stabilizing force in financial markets, providing essential liquidity upon which market participants depend. Across our offices in the US, Europe, Asia Pacific, and India, our talented quant researchers, engineers, traders, and business operations professionals are united by our uniquely collaborative, high-performance culture, and our commitment to giving back. From entering dynamic new markets to embracing disruptive technologies, and from developing an innovative research environment to diversifying our trading strategies, we dare to continuously innovate and collaborate to succeed.
Principal Machine Learning Engineer employer: IMC Trading
Contact Detail:
IMC Trading Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Principal Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at IMC or similar firms. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Show off your skills! Prepare a portfolio or a GitHub repo showcasing your ML projects. When you get that interview, having tangible examples of your work can really set you apart.
✨Tip Number 3
Practice makes perfect! Brush up on your technical skills and be ready for coding challenges. Use platforms like LeetCode or HackerRank to simulate the interview experience.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're genuinely interested in joining the team at IMC.
We think you need these skills to ace Principal Machine Learning Engineer
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter to highlight your experience with ML infrastructure and deep learning frameworks. We want to see how your skills align with the role, so don’t hold back on showcasing your relevant projects!
Showcase Your Impact: When detailing your past experiences, focus on the impact you made in previous roles. We love to see quantifiable results, so if you improved a system's efficiency or reduced training time, let us know how you did it!
Be Clear and Concise: Keep your application clear and to the point. We appreciate well-structured documents that are easy to read. Avoid jargon unless it's necessary, and make sure your passion for machine learning shines through!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re genuinely interested in joining our team!
How to prepare for a job interview at IMC Trading
✨Know Your ML Fundamentals
Brush up on your deep learning fundamentals, especially optimisation, regularisation, and loss design. Be ready to discuss how these concepts apply to large-scale training and inference systems, as they are crucial for the role.
✨Showcase Your Experience
Prepare specific examples from your 8+ years of experience building ML platforms. Highlight projects where you designed and owned large-scale systems, focusing on the impact your work had on the organisation's goals.
✨Familiarise with Modern Architectures
Get comfortable discussing modern architectures like transformers and graph neural networks. Be prepared to explain how you've implemented these in past projects, especially in relation to noisy, high-frequency financial data.
✨Emphasise Collaboration
IMC values collaboration between researchers and engineers. Prepare to share experiences where you partnered with cross-functional teams to accelerate iteration cycles and improve model deployment processes.