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Weekend Machine Learning Software Engineer 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 ... We build the hardware, the software, and the infrastructure, so when you hit a bottleneck, you can ...

Gotion, Inc.

Machine Learning Engineer

Manteno, IL

Role Summary We are seeking a highly motivated Machine Learning Engineer with a strong background in model architecture design and algorithm development, ideally with experience in scientific domains ...

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 ... Experience optimizing ML models for edge deployment (TensorRT, ONNX, quantization) Software ...

Paylocity

Manager Machine Learning Engineering

Schaumburg, IL · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

The company has become one of the fastest-growing HCM software providers worldwide by offering an ... Our machine learning engineering team is responsible for developing infrastructure and tooling to ...

Gotion, Inc.

Machine Learning Engineer

Manteno, IL · On-site

Role Summary We are seeking a highly motivated Machine Learning Engineer with a strong background in model architecture design and algorithm development, ideally with experience in scientific domains ...

Gotion Inc.

Machine Learning Engineer

Manteno, IL · On-site

Role Summary We are seeking a highly motivated Machine Learning Engineer with a strong background in model architecture design and algorithm development, ideally with experience in scientific domains ...

Career Renew

Senior Machine Learning Engineer

Chicago, IL · Remote

$165K - $225K/yr

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a ... Knowledge of software engineering best practices including version control (Git) and CI/CD ...

IMC

Hardware Machine Learning Engineer

Chicago, IL · On-site

$200K - $225K/yr

  • PTO

We build the hardware, the software, and the infrastructure, so when you hit a bottleneck, you can ... Understanding of machine learning fundamentals - neural network architectures, inference ...

IMC

Hardware Machine Learning Engineer

Chicago, IL · On-site

$200K - $225K/yr

  • PTO

We build the hardware, the software, and the infrastructure, so when you hit a bottleneck, you can ... Understanding of machine learning fundamentals - neural network architectures, inference ...

Reliance, Inc.

AI Software Engineer 1

Chicago, IL

$80K - $100K/yr

The AI Software Engineer I is responsible for supporting the development, implementation, and ... Foundational knowledge of artificial intelligence, machine learning, automation, or data-related ...

TEKsystems

Machine Learning Engineer

Chicago, IL · Remote

$95 - $105/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

TEKsystems is seeking a Machine Learning Engineer to support one of our major customers that sits ... software services, not writing research papers. - Cloud engineering experience is required ...

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Showing results 21-40

All JobsMachine Learning Software Engineer JobsWeekend Machine Learning Software Engineer JobsWeekend Machine Learning Software Engineer Jobs in Chicago, IL

Weekend Machine Learning Software Engineer information

See Chicago, IL salary details

$65.4K

$152K

$211.7K

How much do weekend machine learning software engineer jobs pay per year?

As of Aug 19, 2026, the average yearly pay for weekend machine learning software engineer in Chicago, IL is $151,971.00, according to ZipRecruiter salary data. Most workers in this role earn between $123,600.00 and $178,200.00 per year, depending on experience, location, and employer.

What is a weekend machine learning software engineer?

A Weekend Machine Learning Software Engineer is a professional who specializes in developing and deploying machine learning models and software systems, but works primarily on weekends. These engineers often collaborate remotely or part-time, contributing to machine learning projects such as model training, data preprocessing, or integration into applications. The role typically requires strong programming skills, experience with machine learning frameworks, and the ability to work independently. Weekend positions may appeal to individuals seeking flexible schedules or supplemental income, while still engaging in advanced technical work.

What are the typical responsibilities and collaboration expectations for a weekend machine learning software engineer?

As a Weekend Machine Learning Software Engineer, you’ll often focus on addressing project backlogs, refining models, and supporting critical deployments during off-peak hours. You’ll typically collaborate remotely with data scientists, product managers, and other engineers through asynchronous communication or scheduled virtual check-ins. The role requires a high degree of independence and strong documentation skills, as well as the ability to quickly troubleshoot and implement solutions with limited direct supervision. This position is ideal for those who are self-motivated and enjoy contributing to core projects outside the standard workweek.

What are the key skills and qualifications needed to thrive as a weekend machine learning software engineer, and why are they important?

To thrive as a Weekend Machine Learning Software Engineer, you need a solid background in computer science, programming (Python, Java, or C++), and applied mathematics, supported by experience with machine learning algorithms. Familiarity with tools and frameworks such as TensorFlow, PyTorch, scikit-learn, and version control systems like Git is typically required. Strong problem-solving skills, effective time management, and the ability to work independently are vital soft skills in this role. These competencies are essential for efficiently delivering robust machine learning solutions during limited weekend hours and collaborating remotely with teams.

What is the difference between Weekend Machine Learning Software Engineer vs Part-Time Data Scientist?

AspectWeekend Machine Learning Software EngineerPart-Time Data Scientist
CredentialsBachelor's or higher in CS, ML, or related fields; experience with ML frameworksBachelor's or higher in Data Science, Statistics, or related fields; analytical skills
Work EnvironmentTech companies, startups, or research labs; project-based tasksResearch institutions, consulting firms, or corporate analytics teams
Usage in IndustryDeveloping ML models, algorithms, and software solutionsData analysis, modeling, and insights generation

The Weekend Machine Learning Software Engineer primarily focuses on developing and implementing machine learning models during weekends, often in a software engineering context. In contrast, a Part-Time Data Scientist emphasizes analyzing data, building statistical models, and deriving insights, often with a broader focus on data analysis rather than software development. Both roles may overlap in skills but differ in their core responsibilities and work environments.

What are the most commonly searched types of Machine Learning Software Engineer jobs in Chicago, IL?

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

Weekend Machine Learning Software Engineer jobs near you

Hardware Machine Learning Engineer

IMC

Chicago, IL

$127K - $167K/yr

Full-time

Re-posted 6 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