IMC is hiring: Principal Machine Learning Engineer in Chicago
IMC, Chicago, IL, United States
Amsterdam, Netherlands; Chicago, United States; Hong Kong, Hong Kong; London, United Kingdom; New York, United States; Sydney, Australia
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
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
$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.
#J-18808-Ljbffr
In Summary: IMC is 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 .
En Español: En IMC, creemos que la tecnología es el fundamento de nuestra ventaja competitiva y el aprendizaje automático es cada vez más central para cómo negociamos. Durante los últimos años, hemos estado construyendo constantemente nuestras capacidades de aprendizaje automatico: desarrollar infraestructura, aumentar nuestro cluster interno de GPU, implementar modelos en producción y asociarnos estrechamente con investigadores cuánticos y comerciantes para generar un impacto real. Ahora estamos expandiendo el equipo, ampliando nuestros sistemas y acelerando la aplicación del aprendizaje profundo en nuestro flujo de trabajo de investigación y ejecución. Buscamos a un ingeniero principal de Machine Learning para ayudar a dar forma a la próxima fase de influir en nuestras prácticas arquitectónicas, conducir y resolver problemas de alto apalancamiento. Si ha construido una infraestructura ML a gran escala en otros lugares y está buscando un papel donde sus ideas realmente ayuden a dar forma al futuro de nuestra empresa nos encantaría escucharlo. Sus responsabilidades centrales Diseñar y construir una infraestructura integral para la capacitación, evaluación y producción de los modelos ML, trabajando estrechamente con nuestros ingenieros HPC que gestionan nuestro cluster informático on-premise Influir las opciones fundamentales sobre el acceso a datos, orquestación computacional, seguimiento de experimentos, versionamiento de modelo e implementación Explore técnicas como pre-entrenamiento auto supervisado, aprendizaje de representación y modelado transversal donde ofrezcan una verdadera ventaja Formar nuestro enfoque a la reproducibilidad, el aprendizaje continuo y el monitoreo de producción en un entorno de datos a escala de petabytes Definir estándares que crean consistencia entre equipos y geografías; Ingenieros mentores e influyen en la cultura técnica más allá de su trabajo inmediato Manténgase al día con los desarrollos en investigación de aprendizaje profundo y infraestructura de ML; traiga ideas del mundo académico e industria sobre cómo trabajamos Ya sean nuevas arquitecturas, técnicas de capacitación o herramientas Títulos Tus habilidades y Experiencia 8+ años de experiencia construyendo plataformas o infraestructuras en una empresa líder, laboratorio de investigaciones, tensión o IMC. Una trayectoria real de diseño y poseer sistemas de entrenamiento a gran escala y las tecnologías de Amsterdam Tensor no solo contribuye, sino también proficiencias profundas en la práctica, pero aún se deducen prácticas experimentales en desarrollo de conocimientos básicos, así como la creación de modelos de tecnología de construcción en base a nivel mundial (Hong Kong) Optimulará sus experiencias estratégicas en torno a la implementación de sistemas de inteligencia tecnológicos, cuyas aplicaciones son muy sólidas. Las mejores ideas ganan, sin importar de dónde vienen. Conexión estrecha con la investigación No se construirá aisladamente. Investigadores e ingenieros trabajan uno al lado del otro, iterando juntos. #LI-DNP El rango de salario básico para el papel está incluido a continuación.
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
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
$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.
#J-18808-Ljbffr
In Summary: IMC is 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 .
En Español: En IMC, creemos que la tecnología es el fundamento de nuestra ventaja competitiva y el aprendizaje automático es cada vez más central para cómo negociamos. Durante los últimos años, hemos estado construyendo constantemente nuestras capacidades de aprendizaje automatico: desarrollar infraestructura, aumentar nuestro cluster interno de GPU, implementar modelos en producción y asociarnos estrechamente con investigadores cuánticos y comerciantes para generar un impacto real. Ahora estamos expandiendo el equipo, ampliando nuestros sistemas y acelerando la aplicación del aprendizaje profundo en nuestro flujo de trabajo de investigación y ejecución. Buscamos a un ingeniero principal de Machine Learning para ayudar a dar forma a la próxima fase de influir en nuestras prácticas arquitectónicas, conducir y resolver problemas de alto apalancamiento. Si ha construido una infraestructura ML a gran escala en otros lugares y está buscando un papel donde sus ideas realmente ayuden a dar forma al futuro de nuestra empresa nos encantaría escucharlo. Sus responsabilidades centrales Diseñar y construir una infraestructura integral para la capacitación, evaluación y producción de los modelos ML, trabajando estrechamente con nuestros ingenieros HPC que gestionan nuestro cluster informático on-premise Influir las opciones fundamentales sobre el acceso a datos, orquestación computacional, seguimiento de experimentos, versionamiento de modelo e implementación Explore técnicas como pre-entrenamiento auto supervisado, aprendizaje de representación y modelado transversal donde ofrezcan una verdadera ventaja Formar nuestro enfoque a la reproducibilidad, el aprendizaje continuo y el monitoreo de producción en un entorno de datos a escala de petabytes Definir estándares que crean consistencia entre equipos y geografías; Ingenieros mentores e influyen en la cultura técnica más allá de su trabajo inmediato Manténgase al día con los desarrollos en investigación de aprendizaje profundo y infraestructura de ML; traiga ideas del mundo académico e industria sobre cómo trabajamos Ya sean nuevas arquitecturas, técnicas de capacitación o herramientas Títulos Tus habilidades y Experiencia 8+ años de experiencia construyendo plataformas o infraestructuras en una empresa líder, laboratorio de investigaciones, tensión o IMC. Una trayectoria real de diseño y poseer sistemas de entrenamiento a gran escala y las tecnologías de Amsterdam Tensor no solo contribuye, sino también proficiencias profundas en la práctica, pero aún se deducen prácticas experimentales en desarrollo de conocimientos básicos, así como la creación de modelos de tecnología de construcción en base a nivel mundial (Hong Kong) Optimulará sus experiencias estratégicas en torno a la implementación de sistemas de inteligencia tecnológicos, cuyas aplicaciones son muy sólidas. Las mejores ideas ganan, sin importar de dónde vienen. Conexión estrecha con la investigación No se construirá aisladamente. Investigadores e ingenieros trabajan uno al lado del otro, iterando juntos. #LI-DNP El rango de salario básico para el papel está incluido a continuación.