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Machine Learning Engineer Jobs in Hillside, IL (NOW HIRING)

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 ...

AI Machine Learning Engineer

Chicago, IL · Hybrid

$100K - $151K/yr

The Hartford is seeking AI Machine Learning Engineer to build Machine Learning Operations (MLOps) services for the Global Specialty Applied AI team. The Hartford is developing industryleading AI and ...

Senior AI Machine Learning Engineer

Chicago, IL · Hybrid

$126K - $166K/yr

As a Senior Machine Learning Engineer , you will play a critical role in designing, building, and operationalizing productiongrade AI solutions-partnering closely with product, engineering, and ...

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a fully remote role for US/Canada based candidates. Salary range: 165-225K USD yearly plus benefits plus ...

... 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 ...

... 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 ...

... 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 ...

Senior Machine Learning Engineer

Chicago, IL · On-site

$107K - $147K/yr

Our client is looking to bring on a Senior Machine Learning Engineer to help build and scale a nextgeneration voice-centric AI platform used by millions. In this role, you'll own the full ML ...

Sr Machine Learning Engineer

Chicago, IL

$57.50 - $76/hr

D.) in a quantitative discipline such as Statistics, Mathematics, Computer Science, Engineering, or a related field. * Strong knowledge of statistical and machine learning techniques, including but ...

Sr Machine Learning Engineer

Chicago, IL · On-site

$57.50 - $76/hr

D.) in a quantitative discipline such as Statistics, Mathematics, Computer Science, Engineering, or a related field. * Strong knowledge of statistical and machine learning techniques, including but ...

Sr AI Machine Learning Engineer

Chicago, IL · Hybrid

$117K - $175K/yr

The Hartford is seeking Senior AI Machine Learning Engineer to build Machine Learning Operations (MLOps) services for the Global Specialty Applied AI team. The Hartford is developing industryleading ...

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Machine Learning Engineer information

See Hillside, IL salary details

$31K

$126.6K

$190.3K

How much do machine learning engineer jobs pay per year?

As of Jun 24, 2026, the average yearly pay for machine learning engineer in Hillside, IL is $126,635.00, according to ZipRecruiter salary data. Most workers in this role earn between $99,800.00 and $152,400.00 per year, depending on experience, location, and employer.

Is ML full of coding?

Machine Learning Engineers typically do a significant amount of coding, especially in languages like Python or R, to develop algorithms, preprocess data, and build models. Strong programming skills are essential, along with knowledge of frameworks such as TensorFlow or PyTorch, but the role also involves data analysis, model evaluation, and collaboration with teams. Coding is a core component of the job, though some tasks may involve model deployment and optimization that require different skills.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-paying industries such as finance or technology can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially at large tech companies or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as they develop, implement, and maintain AI systems, requiring specialized skills in programming, data analysis, and model optimization. Roles that involve complex problem-solving, creativity, and human interaction—such as healthcare professionals, educators, skilled tradespeople, and certain managerial positions—are also expected to persist despite AI advancements. These jobs typically require emotional intelligence, adaptability, and domain expertise that AI cannot easily replicate.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

What cities near Hillside, IL are hiring for Machine Learning Engineer jobs? Cities near Hillside, IL with the most Machine Learning Engineer job openings:
Infographic showing various Machine Learning Engineer job openings in Hillside, IL as of June 2026, with employment types broken down into 1% As Needed, 94% Full Time, 3% Part Time, 1% Temporary, and 1% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $126,635 per year, or $60.9 per hour.

Principal Machine Learning Engineer

IMC Inc

Chicago, IL • On-site

$200K - $250K/yr

Other

PTO

Posted 17 days ago


Job description

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.