Hardware Machine Learning Engineer

The Hardware Machine Learning Engineer at IMC will join a dynamic team dedicated to pushing the frontiers of low-latency hardware design and state-of-the-art machine learning. IMC is a global market-making firm that leverages advanced technology and scientific research to deliver superior trading performance. This role is integral to IMC’s ongoing efforts to gain a competitive edge by combining novel algorithmic approaches with high-performance hardware.

In this position, you will focus on innovating and implementing hardware-based machine learning solutions deployed on low-latency trading systems. The day-to-day work involves collaborating with software and hardware engineers, developing and optimizing ML models, and translating these models to run efficiently on FPGAs or other custom hardware. You will also profile and fine-tune algorithms, ensuring that performance requirements are met within the constraints of real-world financial markets.

Successful candidates will possess a strong background in hardware acceleration, machine learning, and programming. Proficiency in hardware description languages such as VHDL or Verilog, experience with FPGAs, and a deep understanding of ML frameworks like TensorFlow or PyTorch are essential. Candidates should also have strong analytical and problem-solving skills along with a degree in computer engineering, computer science, electrical engineering, or a related field.

IMC offers a highly competitive compensation package, including attractive salary, performance bonuses, and comprehensive benefits. The company provides access to state-of-the-art technology and opportunities for continued professional growth through dedicated training and mentorship programs.

IMC's fast-paced environment emphasizes innovation, collaboration, and technical excellence. Employees benefit from a flat organizational structure, global career mobility, and a culture that rewards curiosity and commitment. This is an ideal opportunity for engineers who are passionate about the intersection of hardware and machine learning and motivated to make an impact in financial technology.