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Junior Machine Learning Compiler Engineer Jobs in Cedarburg, WI

KOHLS

Senior MLOps Engineer (Remote)

Menomonee Falls, WI · On-site

$104K - $144K/yr

Contribute to the roadmap for Machine Learning Engineering and Data Science tools, including ... developing reusable frameworks and standardized solutions to streamline model implementation

Deloitte

Senior AI Engineer - SFL Scientific

Milwaukee, WI

$103K - $141K/yr

Work with clients to design, develop, and deploy new architectures to support machine learning ... Mentor, motivate, and coach junior members on technical best practices and inspire professional ...

Deloitte

Lead AI and Data Science Engineer II

Milwaukee, WI

$101K - $133K/yr

Mentor junior and mid-level data scientists across the analytics lifecycle, from problem framing ... Data science applies statistical analysis, machine learning, and AI to identify meaningful patterns ...

Pwc

AI & GenAI Data Scientist-Senior Associate

Milwaukee, WI · On-site

$77K - $202K/yr

... junior team members and fostering a collaborative work environment What You Must Have - At least a ... Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS ...

Pwc

CTIO AI Engineering Manager

Milwaukee, WI · On-site

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced ... junior staff while upholding remarkable standards of quality and innovation in deliverables.

CliftonLarsonAllen

Lead ML Ops Engineer

Milwaukee, WI · On-site

$101K - $133K/yr

This role manages a team of Machine Learning Operations Engineers, oversees the end-to-end machine-learning strategy and execution, sets vision for MLOps, and ensures alignment with business goals.

Milwaukee Tool

Firmware Engineer II

Brookfield, WI · On-site

... machine learning, and the use of artificial intelligence. Behind our doors you'll be empowered ... Experience leading projects or mentoring junior firmware engineers. * Excellent problem-solving ...

Milwaukee Tool

Firmware Engineer II

Brookfield, WI · On-site

... machine learning, and the use of artificial intelligence. Behind our doors you'll be empowered ... Experience leading projects or mentoring junior firmware engineers. * Excellentproblem-solving ...

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All JobsMachine Learning Compiler Engineer JobsJunior Machine Learning Compiler Engineer JobsJunior Machine Learning Compiler Engineer Jobs in Cedarburg, WI

Junior Machine Learning Compiler Engineer information

See Cedarburg, WI salary details

$32.9K

$70.6K

$107.7K

How much do junior machine learning compiler engineer jobs pay per year?

As of Aug 9, 2026, the average yearly pay for junior machine learning compiler engineer in Cedarburg, WI is $70,591.00, according to ZipRecruiter salary data. Most workers in this role earn between $47,700.00 and $78,700.00 per year, depending on experience, location, and employer.

What are typical projects and responsibilities for a junior machine learning compiler engineer in a collaborative team setting?

As a Junior Machine Learning Compiler Engineer, you can expect to work on projects that focus on optimizing machine learning models for performance and deployment across various hardware platforms. Typical responsibilities include assisting in developing and debugging compiler passes, implementing optimizations, and contributing to code reviews. You'll frequently collaborate with senior engineers, data scientists, and hardware specialists to ensure that models are efficiently translated and executed. This role offers valuable learning opportunities through hands-on coding, exposure to state-of-the-art ML frameworks, and regular team meetings for knowledge sharing and mentorship.

What does a junior machine learning compiler engineer do?

A Junior Machine Learning Compiler Engineer helps design, develop, and optimize compilers for machine learning models. Their work involves translating high-level machine learning code into efficient low-level code that can run on various hardware platforms, such as CPUs, GPUs, or specialized AI chips. They often collaborate with software engineers and data scientists to ensure that machine learning workloads run efficiently and correctly. This role typically involves programming, debugging, and performance tuning, often using languages like C++, Python, and specialized frameworks.

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

To thrive as a Junior Machine Learning Compiler Engineer, you need a solid background in computer science fundamentals, programming (especially C++ and Python), and foundational knowledge of machine learning and compiler theory. Familiarity with frameworks and tools such as LLVM, TensorFlow, MLIR, and version control systems is typically required, along with a relevant bachelor’s or master’s degree. Strong problem-solving abilities, attention to detail, and effective teamwork and communication skills set standout candidates apart. These skills and qualities are crucial for efficiently optimizing machine learning models for various hardware targets and collaborating on innovative compiler solutions.

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

AspectJunior Machine Learning Compiler EngineerData Scientist
Required CredentialsBachelor's in Computer Science, Software Engineering, or related field; knowledge of compiler design and ML frameworksBachelor's or higher in Data Science, Statistics, Computer Science, or related field; strong analytical skills
Work EnvironmentSoftware development teams, focusing on compiler optimization for ML modelsData analysis teams, focusing on data interpretation and model development
Employer & Industry UsageTech companies, AI startups, hardware firmsTech firms, finance, healthcare, research institutions

The Junior Machine Learning Compiler Engineer primarily focuses on developing and optimizing compilers for machine learning models, requiring programming and compiler knowledge. In contrast, a Data Scientist analyzes data, builds models, and provides insights. Both roles are essential in AI and tech industries but differ in technical focus and daily tasks.

What are the most commonly searched types of Machine Learning Compiler Engineer jobs in Cedarburg, WI? The most popular types of Machine Learning Compiler Engineer jobs in Cedarburg, WI are:
What are popular job titles related to Junior Machine Learning Compiler Engineer jobs in Cedarburg, WI? For Junior Machine Learning Compiler Engineer jobs in Cedarburg, WI, the most frequently searched job titles are:
Junior Machine Learning Compiler Engineer jobs near you

Applied Machine Learning Engineer I - Advanced Engineering & Technology

Milwaukee Tool

Brookfield, WI • On-site

Full-time

Medical, Dental, Vision, Retirement

Posted 28 days ago

Job description

Job Description:
Applicants must be authorized to work in the U.S.; Sponsorship is not available for this position at this time.
INNOVATE WITHOUT BOUNDARIES! At Milwaukee Tool we firmly believe that our People and our Culture are the secrets to our success - so we give you unlimited access to everything you need to create disruptive new technologies and solutions.
Your Role on the Team:
As a member of the Advanced Engineering and Technology (AET) Team in the Power Tool Accessories business unit you will utilize your expertise in machine learning to solve problems where no established solution exists and deliver first-of-its-kind technologies at Milwaukee Tool. You will support the research, prototyping, and delivery of ML-driven capabilities that accelerate how we design and develop products. You will take ideas from conceptual whiteboard architectures through functional prototypes and support hand-off integrations, delivering technology innovation to product and production engineering teams. This role is an individual contributor position focused on applied execution and technology demonstration, working under shared technical direction.
Why This Role is Different:
  • Full-Stack ML in a Physical Domain: Work across the ML stack, from machine and sensor-level data through model deployment on edge hardware or cloud infrastructure.
  • R&D Engineering First: Apply ML across Technology Readiness Levels (TRL 1-7), bringing technology innovation to life beyond model tuning. Domain knowledge in materials, mechanics, signals, or physics is central to this role.
  • Flexible Tools: Select and use frameworks and libraries best suited to the problem, without being constrained to a single ecosystem.
  • Real Impact: Deliver ML-driven capabilities that shorten product development cycles and unlock new engineering possibilities at Milwaukee Tool.

What You'll Do:
  • Research and evaluate emerging AI and ML technologies, advancing them through the Technology Readiness Level (TRL) process from concept through technology integration.
  • Frame engineering problems as ML problems by assessing ML value versus physics-based or analytical approaches and defining practical success criteria.
  • Design, train, and evaluate ML models to help solve well-scoped applied science and engineering problems, working under the guidance of senior engineers.
  • Build ML workflows spanning data acquisition, feature engineering, model development, and validation using standard scientific and ML libraries (NumPy, Pandas, scikit-learn, PyTorch, TensorFlow).
  • Support algorithm selection and the construction of standard feature sets for engineering problems.
  • Support the deployment of ML models on edge hardware and cloud infrastructure, building and deploying with guidance.
  • Deploy ML enabled systems on edge hardware and cloud infrastructure to support engineering decisions.
  • Prepare technology transfer packages by documenting architecture decisions, known limitations, data requirements, and deployment specifications to enable technology adoption.
  • Conduct experiments and data analysis following established patterns and methods; identify and debug basic model errors.
  • Organize, clean, and prepare data for downstream tasks, and create visualizations that support hypotheses, insights, and conclusions.
  • Collaborate with cross-functional teams to deliver ML solutions aligned with engineering needs, and support the design of data collection and test plans.
  • Research and learn about emerging AI and ML technologies through literature, universities, conferences, and vendor engagement.

What You'll Bring:
  • BS in Mechanical Engineering, Electrical Engineering, Materials Science, Physics, Computer Science, Data Science, or related engineering discipline, with advanced coursework or experience in Machine Learning.
  • Experience applying ML to physical-world engineering or scientific problems (materials, mechanical systems, manufacturing, sensor systems, chemical processes, or similar).
  • Demonstrated experience designing, training, and evaluating ML models on real-world or academic problems.
  • Working knowledge of Python and the scientific computing ecosystem (NumPy, SciPy, Pandas, scikit-learn), with familiarity with SQL.
  • Exposure to at least one deep learning framework (PyTorch or TensorFlow), including training models, and awareness of cloud ML platforms (Azure ML, AWS SageMaker, or equivalent).
  • Strong mathematical foundations in linear algebra, probability, statistics, and optimization, with the ability to reason about loss functions, convergence behavior, and model assumptions.
  • Ability to help formulate well-scoped engineering or scientific tasks into ML problems with clear objectives and evaluation criteria, and awareness of when different model classes should be used.
  • Curiosity-driven approach to learning new technologies and methods, with emphasis on applying machine learning to real-world scientific and engineering challenges.
  • Ability to work across a diverse range of data types.
  • Hands-on approach to collaboration and evaluation of technologies.
  • Ability to thrive in an ambiguous and fast-paced environment, where problem definitions evolve.
  • Ability to travel 10% of the time (domestic and international).

Preferred
  • Master's Degree in relevant field.
  • Familiarity with common sensors and interpreting their physical data, and exposure to engineering test lab workflows.
  • Experience with computer vision for engineering applications.
  • Awareness of edge deployment concepts: model optimization and containerized deployment to industrial hardware.
  • Coursework or exposure to design of experiments (DOE), uncertainty quantification, or Bayesian optimization.
  • Familiarity with version control, experiment tracking, and reproducible research practices

Working Environment
  • In-Person, Office Environment, R&D Engineering Lab

Our Perks and Benefits:
  • Robust health, dental and vision insurance plans
  • Generous 401 (K) savings plan
  • Education assistance
  • On-site wellness, fitness center, food, and coffee service
  • And many more, check out our benefits site HERE.

Milwaukee Tool is an equal opportunity employer.

Apply