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Machine Learning Engineer Apprenticeship Jobs in Chicago, IL

Hardware Machine Learning Engineer

Chicago, IL · On-site

$200K - $225K/yr

  • PTO

... 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 ...

Hardware Machine Learning Engineer

Chicago, IL · On-site

$200K - $225K/yr

  • PTO

... 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 ...

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 ...

Sr. Machine Learning Engineer

Chicago, IL · On-site

$107K - $147K/yr

Who We Are Looking For We're hiring a Senior Machine Learning Engineer to design and ship the next generation of voice and conversational AI agents within Realm-X. This role helps define AppFolio ...

Showing results 41-60

Machine Learning Engineer Apprenticeship information

See Chicago, IL salary details

$31.4K

$70.7K

$119K

How much do machine learning engineer apprenticeship jobs pay per year?

As of Aug 18, 2026, the average yearly pay for machine learning engineer apprenticeship in Chicago, IL is $70,685.00, according to ZipRecruiter salary data. Most workers in this role earn between $53,600.00 and $76,700.00 per year, depending on experience, location, and employer.

What is a machine learning engineer apprenticeship?

A Machine Learning Engineer Apprenticeship is a structured training program that combines hands-on work experience with classroom or online learning in the field of machine learning. Apprentices work under the guidance of experienced professionals to develop skills in data analysis, building machine learning models, and deploying algorithms in real-world applications. This apprenticeship is ideal for individuals seeking to enter the field of artificial intelligence without prior extensive experience, as it provides practical training and mentorship. Typically, apprenticeships last from several months to a couple of years and may lead to full-time employment upon successful completion.

What types of projects can I expect to work on during a machine learning engineer apprenticeship?

As a Machine Learning Engineer Apprentice, you can expect to participate in hands-on projects that involve data preprocessing, building and evaluating machine learning models, and collaborating with cross-functional teams such as data scientists and software engineers. Common projects may include developing recommendation systems, automating data analysis tasks, or implementing natural language processing solutions. These experiences provide valuable exposure to real-world datasets and industry-standard tools, helping you build foundational skills for a long-term career in machine learning.

What are the key skills and qualifications needed to thrive as a machine learning engineer apprentice, and why are they important?

To thrive as a Machine Learning Engineer Apprentice, a solid understanding of mathematics, programming (especially Python), and foundational machine learning concepts is essential, often supported by coursework or a degree in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, scikit-learn, and version control systems like Git is typically required. Strong analytical thinking, attention to detail, and the ability to collaborate and communicate complex ideas clearly are valuable soft skills. These abilities are crucial for efficiently developing, testing, and deploying machine learning models while contributing effectively to team projects.

What cities near Chicago, IL are hiring for Machine Learning Engineer Apprenticeship jobs?

Cities near Chicago, IL with the most Machine Learning Engineer Apprenticeship job openings:

Infographic showing various Machine Learning Engineer Apprenticeship job openings in Chicago, IL as of August 2026, with employment types broken down into 87% Full Time, and 13% Contract. Highlights an 79% In-person, and 21% Remote job distribution, with an average salary of $70,685 per year, or $34 per hour.

Hardware Machine Learning Engineer

IMC

Chicago, IL • On-site

$200K - $225K/yr

Other

PTO

Re-posted 5 days ago


Job description

We are deploying machine learning directly onto custom hardware - and we want you to help drive it from the ground up. This is an initiative where you'll have the rare opportunity to architect solutions from scratch, influence technical research direction, and see your work drive real impact in one of the most demanding computing environments in the world.
We build the hardware, the software, and the infrastructure, so when you hit a bottleneck, you can fix it - there's no vendor to wait on and no abstraction layer you're not allowed to touch. If you've ever wanted to push the boundaries of what's computationally possible, this role is for you. We're looking for researchers and experienced engineers from any background. Trading experience is a bonus, not a prerequisite.
Your Core Responsibilities
  • Architect and co-design ML models with traders, quant researchers, and software engineers, treating hardware constraints (latency budgets, resource limits, numerical precision) as first-class design inputs
  • Shape our custom hardware roadmap by translating ML model requirements into concrete architectural decisions
  • Work hands-on with hardware 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 systems and quantization methods, and determine what translates to measurable improvements in our systems

Your Skills and Experience
  • Solid understanding of hardware constraints and design trade-offs (e.g., pipelining, resource utilization, fixed-point arithmetic) that shape how ML models can be efficiently mapped onto FPGAs or custom ASICs
  • Experience with hardware fundamentals, whether through VHDL/SystemVerilog development, HLS tools, or ML-to-hardware frameworks like hls4ml, FINN, or Vitis AI
  • Understanding of machine learning fundamentals - neural network architectures, inference optimization, quantization techniques, ML frameworks such as PyTorch/TensorFlow
  • Proficiency in Python, C++, or similar languages for tooling, testing, and simulation
  • Strong communication skills and ability to work collaboratively across disciplines with both technical and non-technical teams

Nice to Have
  • Exposure to ML compiler infrastructure such as MLIR, TVM, XLA, or similar tools for lowering and optimizing models for hardware targets
  • Background in latency-sensitive or resource-constrained systems including high-frequency trading, particle physics data acquisition, real-time signal processing, or similar domains
  • Familiarity with functional verification methodologies (for example SystemVerilog, UVM, Cocotb)
  • Advanced degree (MS or PhD) in EE, CS, Physics, or related field, or equivalent depth through industry or research experience

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-$225,000 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.