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Deep Learning Engineer Jobs in Illinois (NOW HIRING)

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 ...

They are seeking a highly motivated Machine Learning Engineer to design and implement machine ... Responsibilities : • Design and implement novel machine learning and deep learning models ...

Machine Learning Engineer - Senior Associate

Chicago, IL · On-site

$126.30K - $166.50K/yr

Solid understanding of AI/ML algorithms and techniques, including deep learning and natural ... Familiarity with DevOps practices for software deployment and monitoring. * Experience with SQL and ...

Role Summary We are seeking a highly motivated Machine Learning Engineer with a strong background ... Design and implement novel machine learning and deep learning models tailored to internal research ...

Computer Vision Engineer As a Computer Vision Engineer, you will be part of our AI Team working on ... Hands-on experience applying machine learning and deep learning to vision data, preferably direct ...

Sr. Machine Learning Engineer

Schaumburg, IL · On-site

$103K - $141.40K/yr

Toyota Connected's Mobility team is looking for a Sr. Machine Learning Engineer who will use ... deep learning techniques to create scalable solutions and perform R&D to drive discovery of new ...

Design and implement novel machine learning and deep learning models tailored to internal research ... S. in Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or a closely ...

Comscore, Total Visits, March 2025) Day to Day As a Machine Learning Engineer III you will be a ... Knowledge and practical experience working on Deep Learning Libraries (like Torch, Tensorflow, etc.

Comscore, Total Visits, March 2025) Day to Day As a Machine Learning Engineer III, you will be a ... Knowledge and practical experience working on Deep Learning Libraries (like Torch, Tensorflow, etc.

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Deep Learning Engineer information

See Illinois salary details

$36.8K

$112.3K

$185.6K

How much do deep learning engineer jobs pay per year?

As of May 28, 2026, the average yearly pay for deep learning engineer in Illinois is $112,276.00, according to ZipRecruiter salary data. Most workers in this role earn between $80,400.00 and $146,800.00 per year, depending on experience, location, and employer.

What is a Deep Learning Engineer job?

A Deep Learning Engineer is a specialized software engineer who designs, develops, and optimizes deep learning models. They work with neural networks, large datasets, and frameworks like TensorFlow or PyTorch to build AI systems for tasks like image recognition, natural language processing, and autonomous systems. Their responsibilities include data preprocessing, model training, performance tuning, and deploying models into production. Strong programming skills in Python, knowledge of machine learning algorithms, and experience with GPU acceleration are essential for this role.

What are the key skills and qualifications needed to thrive in the Deep Learning Engineer position, and why are they important?

To thrive as a Deep Learning Engineer, you need a strong background in mathematics, machine learning theory, and programming (especially Python), often supported by a relevant degree in computer science, engineering, or related fields. Proficiency with frameworks such as TensorFlow, PyTorch, Keras, as well as experience with GPUs and cloud platforms, is highly valued, and certifications in AI or deep learning can further enhance your profile. Effective problem-solving, strong collaboration skills, and clear communication are important soft skills for excelling in interdisciplinary teams. These abilities ensure that you can develop robust deep learning models, adapt to evolving technologies, and contribute value in both technical and collaborative settings.

What are the typical daily tasks and responsibilities of a Deep Learning Engineer?

Deep Learning Engineers typically spend their days designing, developing, and optimizing neural network models for tasks like image recognition, natural language processing, or recommendation systems. They preprocess and analyze large datasets, experiment with model architectures, and tune hyperparameters to achieve the best performance. Collaboration is often required with data scientists, product managers, and software engineers to integrate models into real-world applications and scale solutions for production. Additionally, many deep learning engineers review current research, stay updated on advancements in AI, and continuously improve their skills. This role offers a dynamic work environment where learning and innovation are highly encouraged.
What are the most commonly searched types of Deep Learning Engineer jobs in Illinois? The most popular types of Deep Learning Engineer jobs in Illinois are:

Principal Machine Learning Engineer

IMC Inc

Chicago, IL • On-site

$200K - $250K/yr

Other

PTO

This job post has expired today. Applications are no longer accepted.


Job description

Principal Machine Learning Engineer

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.

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.

$200,000 - $250,000 USD

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.