Machine Learning Engineer

IMC; New York (NY); On-site; USD 175,000 - 250,000; Full time

Job summary

IMC is seeking Machine Learning Engineers to advance large-scale ML training and real-time inference for trading applications. You will collaborate with researchers and engineers to build robust systems, optimize GPU acceleration, and push the envelope on distributed workflows.

You will contribute across ML research engineering and ML performance engineering, ensuring scalable model infrastructure and efficient deployment in production environments.

Qualifications

• 3+ years of ML experience focused on training or inference systems.
• Proficiency in Python and production-grade software engineering.
• Strong ML fundamentals and ability to reason about model performance.
• Systems mindset with data-driven performance analysis.
• Excellent collaboration with researchers and engineers.
• Experience with distributed training and large-scale systems.
• Experience with GPUs and low-level optimization.
• Open-source contributions and cloud exposure are a plus.

Responsibilities

• Develop large-scale distributed training pipelines.
• Automate ML experiments, hyperparameter tuning and model retraining.
• Collaborate with HPC to optimise workflows and reduce compute cost.
• Build low-latency inference pipelines for real-time production predictions.
• Maximise performance using GPU hardware and acceleration libraries.
• Extend ML frameworks and open-source tools to improve performance.
• Design scalable model infrastructure for high-volume trading data.
• Evaluate and roll out third-party tools for ML development.

Skills

ML experience

Python production engineering

ML fundamentals

Systems mindset

Collaboration

Distributed training

GPU optimization

Production inference

Numerics at scale

Cloud exposure

Open-source contributions

Tools

PyTorch

TensorFlow

JAX

CUDA

Triton

Job description

Machine Learning Engineers at IMC play a pivotal role in building the systems that drive training and deployment of large-scale ML models across IMC's global operations. They collaborate with quantitative researchers, hardware specialists and software engineers to build robust systems that get the most out of GPU acceleration, distributed computing and the broader open-source ML ecosystem. ML Engineers' work directly shapes how fast we can experiment, iterate and deploy — accelerating the cycle that turns a research idea into a live trading strategy.

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.

As a team, our Machine Learning Engineers broadly work across two areas: ML Research Engineering, focused on the systems and frameworks researchers use to train, evaluate and iterate on models at scale; and ML Performance Engineering, focused on making ML workloads execute as efficiently as possible, typically through GPU and low-level systems optimization. These aren't rigid tracks, and there is overlap between them. You will bring depth of expertise in at least one of these focus areas and collaborate closely with global team members.

Your Core Responsibilities

The responsibilities below are representative of the role — the exact mix depends on your team, area of expertise and location.

ML Research Engineering

• Develop large-scale distributed training pipelines that manage complex datasets and models.
• Partner with quantitative researchers to automate ML experiments, hyperparameter tuning and model retraining.
• Work with HPC specialists to optimise workflows, improve training speed and reduce compute cost.

ML Performance Engineering

• Build and optimise low-latency inference pipelines, ensuring models deliver real-time predictions in production systems.
• Maximise performance in training and inference using GPU hardware and acceleration libraries.

Across both areas

• Extend and improve the internals of machine learning frameworks and open-source tools to raise their performance for our workloads.
• Design scalable model infrastructure capable of handling high-volume trading data and delivering real-time, high-accuracy predictions.
• Evaluate and roll out third-party tools that enhance model development, training and inference capabilities.

Your Skills and Experience

Core, regardless of focus area:

• 3+ years of experience in machine learning, with a focus on training or inference systems.
• Production software engineering — Python, testing discipline and genuine ownership of systems once they ship.
• Machine learning fundamentals — you understand modern ML workloads beyond high-level APIs and can reason about model behaviour, correctness and performance at the level relevant to your area of expertise.
• A systems mindset — you reach for measurement before opinion on performance, scale or reliability issues, and can make sound build-versus-buy decisions.
• Strong collaboration — comfortable explaining the reasoning behind your technical decisions, working through differing perspectives with researchers and adapting your approach when the evidence supports it.

Depth in one or more of the following:

• Distributed training and large-scale systems — parallelism strategy, data pipelines and checkpointing; frameworks such as PyTorch, TensorFlow or JAX.
• GPU performance and low-level optimisation — reasoning about memory, compute and latency at the device level, and optimising performance across kernels, compilers and hardware (e.g. CUDA, Triton or equivalent).
• Production inference systems — hands-on experience building and hardening real-time, low-latency prediction pipelines in high-performance environments.
• Numerics and correctness at scale — low-precision effects, KV caching, batching, and knowing which optimisations stay bit-exact.

Technologies listed above are examples rather than requirements; equivalent depth developed using other stacks or hardware platforms is equally relevant.

Also a plus:

• Exposure to cloud platforms and orchestration tools.
• A track record of contributing to open-source projects in machine learning, data science or distributed systems.

Not sure this is the one? If your strength is mainly in modelling and research direction, our Deep Learning Researcher roles are likely a closer fit. If your strength is in general infrastructure rather than ML-specific systems, take a look at our broader infrastructure engineering roles.

#LI-DNP

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.Please visit Benefits - US | IMC Trading for more comprehensive information.

Salary Range

$175,000 - $250,000 USD

About Us

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 management 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.

IMC is an equal opportunity employer. IMC prohibits discrimination of any type and affords equal employment opportunities to applicants without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity and expression, national origin, age, disability, military or veteran status, status as a victim of domestic violence, and/or any other categories protected by applicable federal, state or local law. Completion of this section is voluntary and will not affect your opportunity for employment or the terms or conditions of your employment. The data collected from these questions will be stored separately from your individual application and will be kept confidential.