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Volunteering Machine Learning Hardware Jobs in Illinois

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 from the ground up. This is an initiative where you'll have the rare opportunity to architect ...

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 from the ground up. This is an initiative where you'll have the rare opportunity to architect ...

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 from the ground up. This is an initiative where you'll have the rare opportunity to architect ...

Lightspeed

AI & Machine Learning Engineer

Northbrook, IL · On-site

$100 - $130/hr

As an AI & Machine Learning Engineer, you will design, build, and deploy the intelligent systems ... Optimize model inference for edge deployment on GPU‑accelerated hardware in production Computer ...

TEKsystems

Machine Learning Engineer

Chicago, IL · Remote

$95 - $105/hr

TEKsystems is seeking a Machine Learning Engineer to support one of our major customers that sits ... Insurance (Voluntary Life & AD&D for the employee and dependents) • Short and long-term ...

Until

Machine Learning Engineer / Scientist

Mundelein, IL · On-site

$140K - $240K/yr

... and rewarming hardware. We are also building out our medical hibernation team to tackle the ... About the Role As a Machine Learning Engineer / Scientist at Until, you will be an early member of ...

IMC Trading

Machine Learning Researcher

Chicago, IL · On-site

$250 - $300/hr

Design and deploy machine learning models to enhance trading performance across various asset ... Completion of this section is voluntary and will not affect your opportunity for employment or the ...

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Volunteering Machine Learning Hardware information

What is the difference between Volunteering Machine Learning Hardware vs Data Scientist?

AspectVolunteering Machine Learning HardwareData Scientist
Required CredentialsTechnical knowledge of hardware, basic programming, volunteer experienceDegree in data science, statistics, or related field; programming skills
Work EnvironmentNon-profit projects, labs, or community settingsCorporate, research institutions, or tech companies
Industry UsageSupporting ML hardware development, open-source projectsAnalyzing data, building models, deriving insights
Search & Comparison IntentUnderstanding hardware volunteering roles vs data science roles

Volunteering Machine Learning Hardware focuses on supporting hardware infrastructure and open-source projects, often in volunteer or community settings. Data Scientists analyze data and develop models for business or research purposes. While both roles involve machine learning, they differ in focus, credentials, and work environment.

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For Volunteering Machine Learning Hardware jobs in Illinois, the most frequently searched job titles are:

What job categories do people searching Volunteering Machine Learning Hardware jobs in Illinois look for?

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Cities in Illinois with the most Volunteering Machine Learning Hardware job openings:

Volunteering Machine Learning Hardware jobs near you
Infographic showing various Volunteering Machine Learning Hardware job openings in Illinois as of August 2026, with employment types broken down into 1% As Needed, 58% Full Time, 34% Part Time, 1% Temporary, and 6% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Hardware Machine Learning Engineer

Chicago, IL

IMC
See more jobs at IMCexternal

$127K - $167K/yr

Full-time

Re-posted 12 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

Apply