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Software Engineer Ai Model Training Jobs in Wisconsin

AMC Health

Senior Software Engineer Applied AI

Madison, WI ยท On-site

$123K - $162K/yr

Senior Software Engineer: Applied AI (Voice Agents & ML Systems) AMC Health Remote (US) Full-time ... End-to-end ML pipelines: feature engineering, model training, and scheduled inference * Imbalanced ...

New

Bank of America

WI ยท On-site

$150 - $210/hr

This role is ideal for an engineer passionate about Generative AI, application development ... Builds and maintains frameworks supporting model development, fineโ€‘tuning, deployment, inference ...

Devitechs

WI ยท On-site

$95 - $135/hr

Build predictive models using large datasets and advanced algorithms. * Develop and optimize Generative AI and Large Language Model (LLM) applications. * Collaborate with software developers to ...

New

Pereview Software

WI ยท On-site

$90 - $120/hr

Pereview Software is seeking an AI Engineer to join our growing Product and Engineering team. This ... Monitor, troubleshoot, and continuously improve AI model performance and system reliability

New

Direct Supply

Senior Software Engineer

Milwaukee, WI ยท On-site

$120K - $159K/yr

In the Senior Software Engineer position, you'll lead the development of intelligent, adaptive, and scalable applications by leveraging AI-powered development tools, machine learning models, and ...

Directsupply

Senior Software Engineer

Milwaukee, WI ยท On-site

$120K - $159K/yr

In the Senior Software Engineer position, you'll lead the development of intelligent, adaptive, and scalable applications by leveraging AI-powered development tools, machine learning models, and ...

Directsupply

Staff Software Engineer

Milwaukee, WI

In the Senior Software Engineer position, you'll lead the development of intelligent, adaptive, and scalable applications by leveraging AI-powered development tools, machine learning models, and ...

Direct Supply, Inc.

Senior Software Engineer

Milwaukee, WI ยท On-site

$120K - $159K/yr

In the Senior Software Engineer position, you'll lead the development of intelligent, adaptive, and scalable applications by leveraging AI-powered development tools, machine learning models, and ...

Direct Supply, Inc.

Senior Software Engineer

Milwaukee, WI ยท On-site

$100 - $130/hr

In the Senior Software Engineer position, you'll lead the development of intelligent, adaptive, and scalable applications by leveraging AI-powered development tools, machine learning models, and ...

Spott Inc.

WI ยท On-site

$79.65 - $125.17/hr

We're looking for a product-obsessed Backend Software Engineer to join us and uphold our standard ... Exciting opportunity to work with the latest technologies and AI models on challenging problems ...

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Software Engineer Ai Model Training information

What does a software engineer AI model training do?

A Software Engineer specializing in AI Model Training is responsible for designing, developing, and optimizing machine learning models. Their work involves preparing and processing large datasets, selecting appropriate algorithms, implementing training pipelines, and evaluating model performance. They collaborate closely with data scientists and other engineers to ensure that AI models are accurate, efficient, and suitable for deployment in real-world applications. Additionally, they may help maintain infrastructure for model training and contribute to research and development of new AI techniques.

What are the key skills and qualifications needed to thrive as a software engineer AI model training?

To excel as a Software Engineer in AI Model Training, you need strong programming skills (especially in Python), a solid grasp of machine learning fundamentals, and typically a degree in computer science or a related field. Experience with frameworks like TensorFlow or PyTorch, familiarity with data processing tools, and sometimes certifications in AI or ML are highly valuable. Analytical thinking, problem-solving, and effective collaboration enhance your ability to develop and refine complex AI models. These skills ensure that AI solutions are robust, scalable, and aligned with organizational goals in a rapidly evolving technological landscape.

What are some common challenges faced by software engineers AI model training, and how can they be addressed?

Software Engineers focusing on AI model training often encounter challenges such as managing large datasets, ensuring data quality, and optimizing model performance. Addressing these issues typically involves close collaboration with data scientists, domain experts, and DevOps engineers to streamline the data pipeline and refine training processes. Staying up to date with the latest advancements in machine learning frameworks and tools can also help overcome technical hurdles. Regular code reviews and cross-functional meetings further support problem-solving and foster a productive work environment.

What is the difference between Software Engineer Ai Model Training vs Data Scientist?

AspectSoftware Engineer Ai Model TrainingData Scientist
Required CredentialsBachelor's in CS, related field; experience with ML frameworksBachelor's or higher in CS, statistics, or related field; strong analytical skills
Work EnvironmentDevelopment teams, AI labs, cloud platformsData analysis, research environments, business units
Employer & Industry UsageTech companies, AI startups, research institutionsTech firms, finance, healthcare, consulting

While both roles involve working with data and machine learning, Software Engineer Ai Model Training focuses on developing and optimizing AI models through coding and engineering practices. Data Scientists analyze data, build models, and generate insights. The roles often collaborate but differ in their core responsibilities and skill sets.

Can I get paid to train AI models?

Yes, software engineers and AI specialists can be paid to train AI models, especially in roles that involve developing, fine-tuning, and optimizing machine learning algorithms. These positions often require knowledge of programming languages like Python, experience with machine learning frameworks, and access to computational resources. Compensation varies based on experience, location, and the complexity of the models being trained.

How to become a software engineer AI model trainer?

To become a software engineer AI model trainer, you should have a strong background in computer science, programming skills in languages like Python, and experience with machine learning frameworks such as TensorFlow or PyTorch. Gaining knowledge in data preprocessing, model evaluation, and working with large datasets is essential, along with relevant certifications or advanced degrees in AI or related fields.

What are popular job titles related to Software Engineer Ai Model Training jobs in Wisconsin?

For Software Engineer Ai Model Training jobs in Wisconsin, the most frequently searched job titles are:

What job categories do people searching Software Engineer Ai Model Training jobs in Wisconsin look for?

The top searched job categories for Software Engineer Ai Model Training jobs in Wisconsin are:

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Cities in Wisconsin with the most Software Engineer Ai Model Training job openings:

Software Engineer Ai Model Training jobs near you

Senior Software Engineer Applied AI

AMC Health

Madison, WI โ€ข On-site

$123K - $162K/yr

Full-time

Posted 2 days ago

New

Job description

Senior Software Engineer: Applied AI (Voice Agents & ML Systems)
AMC Health ๏ฟฝ Remote (US) ๏ฟฝ Full-time
The pitch
We build and operate production AI voice agents that hold real phone conversations in a regulated healthcare setting, plus the machine learning and LLM pipelines around them. This is one seat that spans four disciplines that rarely come together: real-time systems, LLM engineering, traditional machine learning, and serious cloud infrastructure, all in production, all with real consequences. If you are the kind of engineer who gets restless doing one thing, this role is the opposite problem.
What you'll work across
Real-time voice AI
  • Streaming, low-latency speech-to-speech systems built on modern LLMs
  • Telephony and real-time media (call control, live audio streaming)
  • Audio handling and the quirks of real human conversation (interruptions, timing, noise)
  • Concurrency on a latency-sensitive path, where p99 matters and a stall is something a caller hears

LLM engineering
  • Wrapping nondeterministic models in deterministic control so they behave reliably in production
  • Multi-model pipelines, prompt design, and cost/latency budgeting
  • Evaluation harnesses, including LLM-as-judge and automated agent-tests-agent approaches
  • Agentic tooling that gives AI systems safe, structured access to infrastructure

Traditional (non-LLM) machine learning
  • End-to-end ML pipelines: feature engineering, model training, and scheduled inference
  • Imbalanced, messy real-world data; calibration and explainability for non-technical consumers
  • Turning research notebooks into reproducible, auditable production pipelines

Cloud and infrastructure
  • Infrastructure as code across multiple environments (we run on AWS)
  • Managed compute, data, streaming, and orchestration services
  • Security engineering in a regulated setting: encryption, least-privilege access, strict data-handling discipline
  • Observability and telemetry-driven debugging, tracing a production issue from a metric anomaly to root cause

Plus occasional full-stack work on internal tools, and an engineering workflow that leans heavily on AI coding assistants, with human accountability for every change.
What you'll actually do
  • Ship and debug code on a live, real-time voice pipeline where latency and correctness are user-facing
  • Design control systems around LLMs: guardrails, budgets, watchdogs, safe fallbacks
  • Build and operate LLM evaluation and batch-analysis pipelines
  • Own traditional ML workflows from data to scheduled production inference
  • Trace production issues from a metric anomaly to root cause, including building the evidence when the cause is a vendor

Must-haves
  • 7+ years building and operating production backend systems, with strong general-purpose programming skills (we work primarily in Python)
  • Experience running distributed systems in the cloud; comfortable debugging from telemetry to root cause
  • Hands-on production experience with LLMs or generative AI (any provider or framework), plus the judgment to know when not to use a model
  • Working fluency across the traditional machine learning lifecycle (you productionize; you do not need to publish)
  • Disciplined in a regulated environment: small, reviewable changes and careful handling of sensitive data

Nice-to-haves
  • Real-time media or telephony experience
  • Front-end / full-stack ability
  • ML pipeline experience, vector search, or embeddings
  • Fluency with AI coding assistants (our workflows assume them, with human accountability for every change)

How we work
Smallest correct change wins. Every behavior change is validated against the live system. Evidence over opinion in debugging. Code review is rigorous. Safety and privacy gate everything.
Work authorization (no exceptions)
This role is open only to US citizens and lawful permanent residents (Green Card holders). We cannot consider candidates who require visa sponsorship now or in the future, and we are unable to make exceptions of any kind.
How to apply
Please submit both of the following:
  • Your LinkedIn profile URL
  • A phone number where we can reach you

A resume is welcome but optional; the two items above are required.

Apply