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Ml Inference Jobs in Milwaukee, WI (NOW HIRING)

CliftonLarsonAllen

Lead ML Ops Engineer

Milwaukee, WI · On-site

$101K - $133K/yr

... training, inference, evaluation, monitoring, retraining, and governance, including generative AI ... Leadership-level expertise in AI/ML platform engineering, spanning MLOps, LLMOps, and AIOps.

Cliftonlarsonallen

Lead ML Ops Engineer

Milwaukee, WI

$101K - $133K/yr

Oversee enterprisescale AI platforms supporting model training, inference, evaluation, monitoring ... Leadershiplevel expertise in AI/ML platform engineering, spanning MLOps, LLMOps, and AIOps.

Cliftonlarsonallen

Lead ML Ops Engineer

Racine, WI

$96K - $126K/yr

Oversee enterprisescale AI platforms supporting model training, inference, evaluation, monitoring ... Leadershiplevel expertise in AI/ML platform engineering, spanning MLOps, LLMOps, and AIOps.

Johnson Controls

Sr. Software Engineer

Glendale, WI · On-site

$114K - $150K/yr

This Senior AI/ML Engineer role sits at the center of that transformation. You will do two things ... Build and maintain data pipelines, model integration layers, and inference infrastructure for real ...

New

US Bank

Technology Architect

Brookfield, WI · On-site

Experience integrating AI/ML capabilities into enterprise applications (model inference, AI services, decisioning systems) * Strong focus on scalability, resiliency, and high availability , including ...

All JobsMl Inference JobsMl Inference Jobs in Milwaukee, WI

Ml Inference information

See Milwaukee, WI salary details

$36.9K

$120.9K

$193.6K

How much do ml inference jobs pay per year?

As of Jun 24, 2026, the average yearly pay for ml inference in Milwaukee, WI is $120,927.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,000.00 and $134,000.00 per year, depending on experience, location, and employer.

What is ML inference?

ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.

What is the difference between Ml Inference vs Data Scientist?

AspectML InferenceData Scientist
Required CredentialsKnowledge of machine learning models, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentDeploying models in production, real-time data processingData analysis, model development, research
Industry UsageAI product deployment, software companiesResearch institutions, tech firms, consulting

ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.

Which 3 jobs will survive AI?

For ML Inference roles, jobs that require complex problem-solving, creativity, and emotional intelligence are more likely to persist, such as data scientists, AI ethics specialists, and machine learning engineers. These roles involve tasks that are difficult to automate and often require specialized skills, domain knowledge, and critical thinking. Continuous learning and expertise in AI tools and programming languages like Python or TensorFlow can also enhance job security in this field.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, specialized skills in deep learning, and strong industry demand can earn $500,000 or more annually, especially in high-cost-of-living areas or within top tech companies. Achieving this level typically requires advanced degrees, certifications, and a proven track record of impactful projects.

What is a $900,000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers or AI research directors, often requiring advanced skills in deep learning, data science, and experience with tools like TensorFlow or PyTorch. These positions usually involve leadership responsibilities, strategic planning, and may require multiple years of specialized experience or advanced degrees.

Is ML a high paying job?

Machine Learning (ML) inference roles are generally well-paid due to the specialized skills required, such as knowledge of algorithms, programming, and data analysis. Salaries vary based on experience, location, and industry, but they tend to be higher than average for tech positions. Advanced roles often require proficiency with tools like TensorFlow or PyTorch and may include certifications or advanced degrees.

What are some common challenges faced by ML Inference Engineers when deploying models to production?

ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.

What are the key skills and qualifications needed to thrive in ML Inference, and why are they important?

To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.
What are popular job titles related to Ml Inference jobs in Milwaukee, WI? For Ml Inference jobs in Milwaukee, WI, the most frequently searched job titles are:
What job categories do people searching Ml Inference jobs in Milwaukee, WI look for? The top searched job categories for Ml Inference jobs in Milwaukee, WI are:
Ml Inference jobs near you
Lead ML Ops Engineer

Lead ML Ops Engineer

CliftonLarsonAllen

Milwaukee, WI • On-site

$101K - $133K/yr

Full-time

Medical, Dental, Vision, Retirement

Posted 15 days ago

CliftonLarsonAllen rating

7.2

Company rating: 7.2 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

16th of 17 rated bookkeepers and accountants

Job description

LA is a top 10 national professional services firm where our purpose is to create opportunities every day, for our clients, our people, and our communities through industry-focused wealth advisory, digital, audit, tax, consulting, and outsourcing services. Even with more than 8,500 people, 130 U.S. locations, and a global reach, we promise to know you and help you.
CLA is dedicated to building a culture that invites different beliefs and perspectives to the table, so we can truly know and help our clients, communities, and each other.
CLA is growing and seeking to hire an experienced Lead Machine Learning Operations Engineer to join our talented team. 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.
How you'll create opportunities in this role:
• Define and execute an enterprise AI/ML platform strategy, encompassing MLOps, LLMOps, and AIOps, and build reusable frameworks and standards adopted across multiple projects and business units.
• Oversee enterprise-scale AI platforms supporting model training, inference, evaluation, monitoring, retraining, and governance, including generative AI systems.
• Align AI and MLOps initiatives with business objectives, ensuring platforms and pipelines meet scalability, performance, security, regulatory, and cost requirements, including responsible and ethical AI considerations.
• Implement and enforce best practices for model and prompt versioning, monitoring, retraining, and automated workflows, ensuring consistent and reliable AI operations.
• Lead teams delivering shared AI infrastructure, tooling, and platforms, providing day-to-day leadership through coaching, development, and performance management.
• Ensure platform reliability and operational excellence by overseeing escalated issue resolution, maintaining high-quality documentation, and driving continuous improvement.
• Track and evaluate industry trends in AI platforms, LLM ecosystems, and AI operations, translating insights into roadmap decisions and platform evolution.
What you will need:
6 years of relevant experience required.
  • Experience in MLOps, DevOps, or related fields, with a focus on enterprise-level solutions preferred.
  • Supervisory experience preferred.

Education
Bachelor's degree is required. Combination of relevant experience, education, and training may be accepted in lieu of degree.
  • Degree in computer science, data science, or related field preferred.

Technical Competencies
  • Advanced proficiency in Python and architectural mastery of object-oriented design across dynamically typed languages.

  • Broad experience integrating and governing multi-language systems, including Python, JavaScript/TypeScript, and enterprise platforms (e.g., .NET).
  • Leadership-level expertise in AI/ML platform engineering, spanning MLOps, LLMOps, and AIOps.
  • Ability to define and enforce enterprise standards for AI model lifecycle management, monitoring, reliability, and cost control.
  • Deep understanding of AI system observability, including drift detection, evaluation frameworks, and incident response.
  • Strong experience with cloud architecture, security, compliance, and enterprise-scale deployments.
  • Proven ability to guide teams in technical decision-making and platform strategy.

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Wellness at CLA
To support our CLA family members, we focus on their physical, financial, social, and emotional well-being and offer comprehensive benefit options that include health, dental, vision, 401k and much more.
To view a complete list of benefits, click here.

What CliftonLarsonAllen employees say

Pay

Benefits

Hours and flexibility

Workplace

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About CliftonLarsonAllen

Sourced by ZipRecruiter

CliftonLarsonAllen (CLA) is a leading professional services company based in Minneapolis, MN, US. CLA operates in the accounting industry and offers a broad range of products and services such as wealth advisory, outsourcing, audit, tax, and consulting services. The company was founded in 1953 with a merger between two firms, Clifton Gunderson and LarsonAllen, in 2012. Working in accordance with their mission to create opportunities for clients, people, and communities, they have established a presence across the US, serving privately held businesses, non-profits, and governmental entities. Recognized for their contributions, CLA has received accolades such as the Innovative Firm of the Year award.

Industry

Accounting services

Company size

5,001 - 10,000 Employees

Headquarters location

Minneapolis, MN, US

Year founded

2012

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