IMC Trading Jobs

Hardware Machine Learning Engineer

IMC Trading

Hardware Machine Learning Engineer

Posted 43 Minutes Ago

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2 Locations

Hybrid

Entry level

Fintech • Machine Learning • Software • Financial Services

IMC is a proprietary trading firm powered by research and technology.

The Role

Architect and co-design machine learning models for custom hardware, accounting for latency, resource, and numerical constraints. Translate model requirements into hardware architecture decisions, collaborate with hardware engineers to implement and verify inference solutions, and deploy systems from proof of concept through production. Evaluate research in neural architecture search, ML systems, quantization, and compiler infrastructure for measurable performance improvements.

Summary Generated by Built In

We are deploying machine learning directly onto custom hardware - and we want you to help drive it from the ground up. This is an initiative where you'll have the rare opportunity to architect solutions from scratch, influence technical research direction, and see your work drive real impact in one of the most demanding computing environments in the world.
We build the hardware, the software, and the infrastructure, so when you hit a bottleneck, you can fix it - there's no vendor to wait on and no abstraction layer you're not allowed to touch. If you've ever wanted to push the boundaries of what's computationally possible, this role is for you. We're looking for researchers and experienced engineers from any background. Trading experience is a bonus, not a prerequisite.
Your Core Responsibilities

Architect and co-design ML models with traders, quant researchers, and software engineers, treating hardware constraints (latency budgets, resource limits, numerical precision) as first-class design inputs
Shape our custom hardware roadmap by translating ML model requirements into concrete architectural decisions
Work hands-on with hardware engineers to implement, verify, and deploy ML inference solutions from proof-of-concept through production
Track and evaluate emerging research in neural architecture search, machine learning systems and quantization methods, and determine what translates to measurable improvements in our systems

Your Skills and Experience

Solid understanding of hardware constraints and design trade-offs (e.g., pipelining, resource utilization, fixed-point arithmetic) that shape how ML models can be efficiently mapped onto FPGAs or custom ASICs
Experience with hardware fundamentals, whether through VHDL/SystemVerilog development, HLS tools, or ML-to-hardware frameworks like hls4ml, FINN, or Vitis AI
Understanding of machine learning fundamentals - neural network architectures, inference optimization, quantization techniques, ML frameworks such as PyTorch/TensorFlow
Proficiency in Python, C++, or similar languages for tooling, testing, and simulation
Strong communication skills and ability to work collaboratively across disciplines with both technical and non-technical teams

Nice to Have

Exposure to ML compiler infrastructure such as MLIR, TVM, XLA, or similar tools for lowering and optimizing models for hardware targets
Background in latency-sensitive or resource-constrained systems including high-frequency trading, particle physics data acquisition, real-time signal processing, or similar domains
Familiarity with functional verification methodologies (for example SystemVerilog, UVM, Cocotb)
Advanced degree (MS or PhD) in EE, CS, Physics, or related field, or equivalent depth through industry or research experience

Skills Required

Understanding of hardware constraints and design trade-offs, including pipelining, resource utilization, and fixed-point arithmetic
Experience with hardware fundamentals through VHDL, SystemVerilog, HLS tools, or ML-to-hardware frameworks such as hls4ml, FINN, or Vitis AI
Understanding of machine learning fundamentals, neural network architectures, inference optimization, and quantization techniques
Experience with ML frameworks such as PyTorch or TensorFlow
Proficiency in Python, C++, or similar languages for tooling, testing, and simulation
Strong communication and cross-disciplinary collaboration skills
Exposure to ML compiler infrastructure such as MLIR, TVM, or XLA
Background in latency-sensitive or resource-constrained systems
Familiarity with functional verification methodologies such as SystemVerilog, UVM, or Cocotb
Advanced degree in electrical engineering, computer science, physics, or a related field, or equivalent industry or research experience

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The Company

HQ: Amsterdam

1,954 Employees

Year Founded: 1989

What We Do

IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing force in markets since 1989, we provide liquidity across trading venues, delivering the best outcome in value and risk to investors. Using our own technology and capital, we build proprietary systems and algorithms that operate across global markets. Our researchers, traders, and engineers work as a collective, combining rapid experimentation, advanced infrastructure, and real-time feedback to turn insight into execution and execution into advantage.

Why Work With Us

At IMC, the best ideas win, regardless of hierarchy. Graduates receive mentorship and make an impact from day one, while experienced hires get to shape their own path in a flexible, high-performance environment. We remove barriers so everyone can grow and help drive one of the world’s leading liquidity providers.