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Summer Machine Learning Hardware 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 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 ...

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 ...

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All JobsSummer Machine Learning Hardware JobsSummer Machine Learning Hardware Jobs in Chicago, IL

Summer Machine Learning Hardware information

See Chicago, IL salary details

$12

$25

$49

How much do summer machine learning hardware jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for summer machine learning hardware in Chicago, IL is $25.33, according to ZipRecruiter salary data. Most workers in this role earn between $18.08 and $28.70 per hour, depending on experience, location, and employer.

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

AspectSummer Machine Learning HardwareSummer Data Scientist
Required CredentialsBachelor's or Master's in Computer Science, Electrical Engineering, or related fields; knowledge of hardware design and programmingBachelor's or Master's in Data Science, Statistics, or related fields; proficiency in data analysis and programming
Work EnvironmentHardware labs, R&D centers, tech companies focusing on AI hardwareOffice settings, research labs, tech companies analyzing data and building models
Industry UsageAI hardware development, embedded systems, hardware acceleration for MLData analysis, predictive modeling, AI application development

Summer Machine Learning Hardware roles focus on designing and optimizing hardware for machine learning applications, requiring technical skills in hardware engineering. In contrast, Summer Data Scientist positions involve analyzing data, building models, and deriving insights. Both roles are essential in AI development but differ in their technical focus and work environment.

What cities near Chicago, IL are hiring for Summer Machine Learning Hardware jobs? Cities near Chicago, IL with the most Summer Machine Learning Hardware job openings:
Summer Machine Learning Hardware jobs near you

Hardware Machine Learning Engineer

IMC

Chicago, IL

$127K - $167K/yr

Full-time

Re-posted 26 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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