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Temporary Machine Learning Engineer Jobs in Naperville, IL

... 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 ...

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 ...

... 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 ...

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 ...

Staff Machine Learning Engineer

Chicago, IL · Hybrid

$169K - $291K/yr

... machine learning models and algorithms to solve complex problems. You will work closely with data scientists, software engineers, and product teams to enhance services through innovative AI/ML ...

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Temporary Machine Learning Engineer information

See Naperville, IL salary details

$31.5K

$128.6K

$193.2K

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

As of Jul 14, 2026, the average yearly pay for temporary machine learning engineer in Naperville, IL is $128,577.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,300.00 and $154,800.00 per year, depending on experience, location, and employer.

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

AspectTemporary Machine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related fields; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related fields; strong analytical skills
Work EnvironmentProject-based, often contract roles in tech or finance companiesResearch and analysis-focused, in tech, finance, or healthcare sectors
Employer UsageUsed for short-term ML projects, model deployment, or prototypingUsed for data analysis, insights, and predictive modeling

Temporary Machine Learning Engineers focus on implementing and deploying ML models on a short-term basis, often within project deadlines. Data Scientists analyze data to generate insights and develop models but may have a broader scope. Both roles require strong technical skills, but their primary functions differ in scope and application.

What are the most commonly searched types of Machine Learning Engineer jobs in Naperville, IL? The most popular types of Machine Learning Engineer jobs in Naperville, IL are:
What are popular job titles related to Temporary Machine Learning Engineer jobs in Naperville, IL? For Temporary Machine Learning Engineer jobs in Naperville, IL, the most frequently searched job titles are:
What job categories do people searching Temporary Machine Learning Engineer jobs in Naperville, IL look for? The top searched job categories for Temporary Machine Learning Engineer jobs in Naperville, IL are:
What cities near Naperville, IL are hiring for Temporary Machine Learning Engineer jobs? Cities near Naperville, IL with the most Temporary Machine Learning Engineer job openings:
Hardware Machine Learning Engineer

Hardware Machine Learning Engineer

IMC

Chicago, IL

$127K - $167K/yr

Other

Re-posted 7 hours 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