ZipRecruiter

Log In

1

Evening Machine Learning Engineer Biotech Jobs in Chicago, IL

IMC

Hardware Machine Learning Engineer

Chicago, IL

$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 ...

IMC

Hardware Machine Learning Engineer

Chicago, IL · On-site

$200K - $225K/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 ...

IMC

Hardware Machine Learning Engineer

Chicago, IL · On-site

$200K - $225K/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 ...

The Hartford

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 ...

AppFolio

Sr. Machine Learning Engineer

Chicago, IL

$107K - $147K/yr

Who We Are Looking For We're hiring a Senior Machine Learning Engineer to design and ship the next generation of voice and conversational AI agents within Realm-X. This role helps define AppFolio ...

previous page

Showing results 41-60

All JobsEvening Machine Learning Engineer Biotech JobsEvening Machine Learning Engineer Biotech Jobs in Chicago, IL

Evening Machine Learning Engineer Biotech information

See Chicago, IL salary details

$32.5K

$132.7K

$199.3K

How much do evening machine learning engineer biotech jobs pay per year?

As of Aug 8, 2026, the average yearly pay for evening machine learning engineer biotech in Chicago, IL is $132,651.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,600.00 and $159,700.00 per year, depending on experience, location, and employer.

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

AspectEvening Machine Learning Engineer BiotechEvening Data Scientist Biotech
Required CredentialsBachelor's or Master's in Computer Science, Data Science, or related fields; experience with ML frameworksBachelor's or Master's in Data Science, Statistics, or related fields; proficiency in data analysis tools
Work EnvironmentDeveloping ML models, coding, deploying algorithms in biotech settingsAnalyzing datasets, creating reports, interpreting data in biotech companies
Employer & Industry UsageBiotech firms, research labs, pharmaceutical companiesBiotech firms, research institutions, healthcare organizations

While both roles involve working with data in biotech, the Evening Machine Learning Engineer Biotech focuses on developing and deploying machine learning models, whereas the Evening Data Scientist Biotech emphasizes data analysis and interpretation. Both roles require strong technical skills and are integral to biotech innovation, but they differ in daily tasks and technical focus.

What are the most commonly searched types of Machine Learning Engineer Biotech jobs in Chicago, IL? The most popular types of Machine Learning Engineer Biotech jobs in Chicago, IL are:
Evening Machine Learning Engineer Biotech jobs near you

Hardware Machine Learning Engineer

IMC

Chicago, IL

$127K - $167K/yr

Full-time

Re-posted 24 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