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Temporary Machine Learning Engineer Jobs in Skokie, IL

Salary Range $200,000-$225,000 USD About Us IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing ...

Senior AI Machine Learning Engineer

Chicago, IL · Hybrid

$126K - $166K/yr

As a Senior Machine Learning Engineer , you will play a critical role in designing, building, and operationalizing productiongrade AI solutions-partnering closely with product, engineering, and ...

Hardware Machine Learning Engineer

Chicago, IL · On-site

$127K - $167K/yr

... 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 ...

Salary Range $200,000-$225,000 USD About Us IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing ...

Machine Learning Engineer

Chicago, IL · On-site

$62K - $100K/yr

As an AI Engineering team member, you will be instrumental in advancing new features and/or solutions from the Proof of Concept stage to full production readiness. Your role involves refining and ...

As an AI Engineering team member, you will be instrumental in advancing new features and/or solutions from the Proof of Concept stage to full production readiness. Your role involves refining and ...

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 ...

Showing results 41-60

Temporary Machine Learning Engineer information

See Skokie, IL salary details

$30.1K

$123K

$184.8K

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

As of Sep 5, 2026, the average yearly pay for temporary machine learning engineer in Skokie, IL is $122,958.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,900.00 and $148,000.00 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 Skokie, IL?

The most popular types of Machine Learning Engineer jobs in Skokie, IL are:

What are popular job titles related to Temporary Machine Learning Engineer jobs in Skokie, IL?

For Temporary Machine Learning Engineer jobs in Skokie, IL, the most frequently searched job titles are:

What job categories do people searching Temporary Machine Learning Engineer jobs in Skokie, IL look for?

The top searched job categories for Temporary Machine Learning Engineer jobs in Skokie, IL are:

What cities near Skokie, IL are hiring for Temporary Machine Learning Engineer jobs?

Cities near Skokie, IL with the most Temporary Machine Learning Engineer job openings:

Infographic showing various Temporary Machine Learning Engineer job openings in Skokie, IL as of July 2026, with employment types broken down into 81% Full Time, and 19% Contract. Highlights an 91% In-person, and 9% Remote job distribution, with an average salary of $122,958 per year, or $59.1 per hour.

Hardware Machine Learning Engineer

IMC

Chicago, IL • On-site

$200K - $225K/yr

Full-time

PTO

Re-posted 23 days ago


Key responsibilities

  • Architect and co-design ML models with traders, quant researchers, and software engineers, considering hardware constraints.

  • Work with hardware engineers to implement, verify, and deploy ML inference solutions from proof-of-concept to production.

  • Track and evaluate emerging research in neural architecture search, machine learning systems, and quantization methods to improve systems.


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 research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing force in markets since 1989, we provide liquidity across trading venues, delivering the best outcome in value and risk management to investors. Using our own technology and capital, we build proprietary systems and algorithms that operate across global markets. Our researchers, traders, and engineers work as a collective, combining rapid experimentation, advanced infrastructure, and real-time feedback to turn insight into execution and execution into advantage.