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Machine Learning Engineer Quantization Jobs in Wood Dale, IL

The role involves designing and deploying machine learning models, collaborating with trading teams ... Required : • PhD or Master's in Engineering, Math, Statistics, Computer Science, or related ...

... machine learning & deep learning to solve challenging trading problems. This role is part of a ... The ideal candidate will have experience working with other researchers and engineers to build and ...

Software Engineer, Machine Learning Responsibilities: * Collaborate with cross-functional teams (product, design, operations, infrastructure) to build innovative application experiences * Implement ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Machine Learning Tutor

Chicago, IL · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

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Showing results 1-20

Machine Learning Engineer Quantization information

See Wood Dale, IL salary details

$31.4K

$128.3K

$192.8K

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

As of Jul 19, 2026, the average yearly pay for machine learning engineer quantization in Wood Dale, IL is $128,293.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,100.00 and $154,400.00 per year, depending on experience, location, and employer.

What are some common challenges Machine Learning Engineers face when implementing quantization techniques in production models?

Machine Learning Engineers working on quantization often encounter challenges such as balancing reduced model size and computational efficiency with maintaining acceptable accuracy levels. Adapting quantization methods to different hardware platforms can also require significant testing and optimization. Additionally, engineers must frequently address compatibility issues with existing deployment pipelines and ensure that quantization-aware training is properly integrated to minimize performance degradation. Collaboration with hardware and software teams is essential to streamline deployment and achieve optimal results.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer Quantization, and why are they important?

To thrive as a Machine Learning Engineer Quantization, you need a solid background in machine learning, deep learning, and computer science, typically supported by a degree in a related field. Familiarity with quantization techniques, frameworks such as TensorFlow Lite or PyTorch, and experience with hardware accelerators are crucial. Strong problem-solving skills, attention to detail, and effective collaboration set top performers apart. These capabilities are vital for efficiently deploying high-performing models on resource-constrained devices and ensuring scalable, real-world AI solutions.

What does a Machine Learning Engineer Quantization do?

A Machine Learning Engineer specializing in quantization focuses on optimizing machine learning models by reducing their size and computational requirements without significantly sacrificing accuracy. This involves converting model parameters and computations from high-precision formats (like 32-bit floating point) to lower-precision formats (such as 8-bit integers). Quantization enables faster inference, lower memory usage, and allows models to run efficiently on edge devices and mobile platforms. These engineers work closely with data scientists and hardware teams to implement, test, and validate quantized models in production environments.

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

AspectMachine Learning Engineer QuantizationData Scientist
Required CredentialsBachelor's or master's in CS, ML, or related; certifications in ML or AIBachelor's or master's in statistics, CS, or related; certifications in data analysis or statistics
Work EnvironmentDeveloping optimized ML models, deploying quantized models for efficiencyAnalyzing data, building predictive models, interpreting results
Industry UsageTech companies, AI hardware firms, embedded systemsFinance, healthcare, marketing, research institutions

Machine Learning Engineer Quantization focuses on optimizing ML models for deployment efficiency, often working closely with hardware and software teams. Data Scientists analyze data and build models for insights. While both roles require ML knowledge, quantization engineers specialize in model compression techniques, whereas data scientists focus on data analysis and interpretation.

What are popular job titles related to Machine Learning Engineer Quantization jobs in Wood Dale, IL? For Machine Learning Engineer Quantization jobs in Wood Dale, IL, the most frequently searched job titles are:
What cities near Wood Dale, IL are hiring for Machine Learning Engineer Quantization jobs? Cities near Wood Dale, IL with the most Machine Learning Engineer Quantization job openings:
Staff/Senior Machine Learning Engineer, Clinical AI

Staff/Senior Machine Learning Engineer, Clinical AI

Tempus

Chicago, IL • On-site, Remote

$170K - $230K/yr

Full-time

Posted 3 days ago


Job description

Passionate about precision medicine and advancing the healthcare industry?

Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time.

We're seeking a highly skilled and innovative Staff/Senior Machine Learning Engineer to join our Clinical AI Team. As a Staff/Senior Machine Learning Engineer, you'll play a crucial role in leveraging and deploying cutting-edge natural language processing models and LLMs specifically tailored for healthcare applications at scale. Your work will contribute to optimizing clinical workflows, improving clinical trial matching, and advancing medical research. This position offers an exciting opportunity to leverage the power of natural language processing and LLMs to revolutionize healthcare and make a significant impact on people's lives.

What You Will Do:

  • Build and operate production AI pipelines: LLM-powered extraction, batch orchestration, and inference, with a focus on reliability, cost, and latency

  • Design and maintain Airflow-based orchestration for batch clinical workflows

  • Build the observability (metrics, logging, alerting) that catches regressions before they reach downstream consumers

  • Build and maintain eval infrastructure that measures clinical model output quality continuously: regression detection, drift, gold-set management, dashboards

  • Ship platform tooling and SDKs that accelerate Machine Learning Scientists and downstream consumers

  • Partner with Machine Learning Scientists to debug bad model outputs to root cause (data, prompt, or pipeline)

  • Participate in the pod's on-call rotation

  • Collaborate with platform / infrastructure teams to leverage GCP services for performance, security, and cost-efficiency

  • Author and review design docs for cross-pod work

  • Raise the engineering bar through code review and design review

Required Qualifications:

  • Strong command of Python in production environments

  • Experience designing, building, and integrating with microservices in production

  • Deployed data orchestration workflows in production (Airflow or equivalent)

  • Worked on cloud-native services (GCP preferred but not required)

  • Built monitoring, observability, and alerting for production systems

  • Hands-on experience with at least one major ML framework - we primarily use LangGraph; PyTorch, spaCy, or equivalents are equally welcome

  • Strong written and verbal communication, including experience authoring and reviewing design docs (RFCs, PRDs, or equivalent); partners well with research scientists, PMs, and clinicians

Preferred Qualifications:

  • Operated production systems hands-on - on-call rotations, incident response, postmortems

  • Experience building eval / quality measurement systems for ML or LLM outputs

  • Hands-on production LLM application experience (prompts, agents, RAG, LLM evals, extraction pipelines)

  • Built internal platforms or SDKs that other engineers / scientists depended on

  • Experience working with clinical or biomedical data (EHR, genomics, pathology, clinical notes)

  • Contributions to relevant open-source projects

#LI-BL1

New York Pay Range - $170,000-$230,000USD

California Pay Range - $170,000-$230,000USD

Illinois Pay Range - $150,000-$210,000USD

Remote - USA Range - $150,000-$210,000USD

The expected salary range above is applicable if the role is performed from California and may vary for other locations (Colorado, Illinois, New York). Actual salary may vary based on qualifications and experience. Tempus offers a full range of benefits, which may include incentive compensation, restricted stock units, medical and other benefits depending on the position.

Additionally,for remote roles open to individuals in unincorporated Los Angeles - including remote roles-Tempus reasonably believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment: engaging positively with customers and other employees; accessing confidential information, including intellectual property, trade secrets, and protected health information; and appropriately handling such information in accordance with legal and ethical standards. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.

We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.