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Apprentice Machine Learning Testing Jobs in Chicago, IL

IMC

Hardware Machine Learning Engineer

Chicago, IL

$127K - $167K/yr

We are deploying machine learning directly onto custom hardware - and we want you to help drive it ... Proficiency in Python, C++, or similar languages for tooling, testing, and simulation * Strong ...

IMC

Hardware Machine Learning Engineer

Chicago, IL · On-site

$200K - $225K/yr

We are deploying machine learning directly onto custom hardware - and we want you to help drive it ... Proficiency in Python, C++, or similar languages for tooling, testing, and simulation * Strong ...

IMC

Hardware Machine Learning Engineer

Chicago, IL · On-site

$200K - $225K/yr

We are deploying machine learning directly onto custom hardware - and we want you to help drive it ... Proficiency in Python, C++, or similar languages for tooling, testing, and simulation * Strong ...

Whiztek Corp

AI Engineer

Schaumburg, IL · On-site

Agentic AI/AI Engineer - Generative AI & Machine Learning Location:  Schaumburg, IL (Hybrid - 3 ... Familiar with functional and non-functional testing of AI/ML applications and operationalizing it ...

TSMG

AI/ML Data Contributor

Chicago, IL

In this role, you will participate in tasks that help improve machine learning models, including data labeling, content evaluation, and user-based testing. Projects may vary in scope and format ...

TSMG

AI/ML Data Contributor

Chicago, IL

In this role, you will participate in tasks that help improve machine learning models, including data labeling, content evaluation, and user-based testing. Projects may vary in scope and format ...

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Apprentice Machine Learning Testing information

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How much do apprentice machine learning testing jobs pay per hour?

As of Jul 9, 2026, the average hourly pay for apprentice machine learning testing in Chicago, IL is $19.94, according to ZipRecruiter salary data. Most workers in this role earn between $16.83 and $21.78 per hour, depending on experience, location, and employer.

What kinds of projects or tasks can I expect to work on as an Apprentice Machine Learning Testing?

As an Apprentice Machine Learning Testing, you’ll typically assist in evaluating machine learning models by designing and running tests, analyzing model outputs, and helping identify issues like bias or overfitting. You may work closely with data scientists and software engineers to validate model performance and ensure results align with project objectives. Your daily tasks might include preparing test datasets, executing automated testing scripts, and documenting findings to help improve model reliability. This role often serves as a valuable introduction to practical machine learning workflows and quality assurance processes in technical teams.

What are the key skills and qualifications needed to thrive as an Apprentice Machine Learning Testing, and why are they important?

To thrive as an Apprentice in Machine Learning Testing, a foundational understanding of statistics, programming (especially Python), and basic machine learning concepts is essential, often supported by a degree or coursework in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, Jupyter Notebooks, and version control systems is typically required. Strong analytical thinking, attention to detail, and effective communication skills help apprentices collaborate and identify testing issues efficiently. These skills ensure accurate model validation, effective troubleshooting, and contribute to the robust deployment of machine learning solutions.

What does an Apprentice Machine Learning Testing do?

An Apprentice Machine Learning Testing professional assists in evaluating and validating machine learning models to ensure they perform as expected. They typically work under the guidance of experienced data scientists or engineers, running tests, analyzing results, and helping to identify issues such as bias or inaccuracies in algorithms. Their responsibilities may also include developing test cases, writing reports, and learning about data preprocessing and evaluation metrics. This role is ideal for those who are new to the field and want to build foundational skills in machine learning quality assurance.

What is the difference between Apprentice Machine Learning Testing vs Machine Learning Engineer?

AspectApprentice Machine Learning TestingMachine Learning Engineer
Required CredentialsBasic understanding of ML concepts, often pursuing relevant certifications or degreesAdvanced degrees (BSc, MSc, PhD) in CS or related fields, with extensive experience
Work EnvironmentEntry-level, supervised testing environments, often in training programsFull-time, independent development and deployment of ML models in production
Employer & Industry UsageInternships, training programs, entry-level roles in tech companiesEstablished tech firms, startups, research institutions

Apprentice Machine Learning Testing roles focus on learning and assisting with testing ML models under supervision, while Machine Learning Engineers design, build, and deploy ML systems independently. The apprentice position is ideal for gaining foundational skills, whereas the engineer role requires advanced expertise and experience.

What are the most commonly searched types of Machine Learning Testing jobs in Chicago, IL? The most popular types of Machine Learning Testing jobs in Chicago, IL are:
What are popular job titles related to Apprentice Machine Learning Testing jobs in Chicago, IL? For Apprentice Machine Learning Testing jobs in Chicago, IL, the most frequently searched job titles are:
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Apprentice Machine Learning Testing jobs near you
Hardware Machine Learning Engineer

Hardware Machine Learning Engineer

IMC

Chicago, IL

$127K - $167K/yr

Other

Re-posted 25 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

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