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Ai For Science Jobs in Wisconsin (NOW HIRING)

Farm Systems

AI Engineer

Watertown, WI

This is a strategic, hands-on position for an experienced technical leader who has a track record ... Bachelor of Science in Software Engineering, Computer Science, Data Science, AI/ML or related field ...

New

AgSource

AI Engineer

Watertown, WI · On-site +1

This is a strategic, hands-on position for an experienced technical leader who has a track record ... Bachelor of Science in Software Engineering, Computer Science, Data Science, AI/ML or related field ...

New

Urus

AI Engineer

Watertown, WI

This is a strategic, hands-on position for an experienced technical leader who has a track record ... Bachelor of Science in Software Engineering, Computer Science, Data Science, AI/ML or related field ...

New

Urus Group LP

AI Engineer

Watertown, WI · On-site +1

This is a strategic, hands-on position for an experienced technical leader who has a track record ... Bachelor of Science in Software Engineering, Computer Science, Data Science, AI/ML or related field ...

New

AgSource

AI Engineer

Watertown, WI

This is a strategic, hands-on position for an experienced technical leader who has a track record ... Bachelor of Science in Software Engineering, Computer Science, Data Science, AI/ML or related field ...

New

Johnson Controls

Ai Engineer

Milwaukee, WI

This role is ideal for an engineer who combines solid software, data, and ML engineering skills with hands-on Generative AI experience-and a data scientist's curiosity for how models behave. You ...

Deloitte

Senior AI Engineer - SFL Scientific

Milwaukee, WI · On-site

$103K - $141K/yr

... for AI investment and identify unique opportunities to transform their businesses. Qualifications Required: * Bachelor's degree in a STEM field (Computer Science, Engineering, Physics, etc.) or ...

Whiterabbit.ai

We are looking for a Research Scientist Intern to push the state of the art of our AI models. As a Research Scientist Intern at Whiterabbit.ai, you will: * Play a key role in architecting the ...

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Ai For Science information

How does collaboration typically work between AI for Science professionals and domain experts in research teams?

AI for Science professionals frequently work closely with experts in fields such as biology, chemistry, or physics to identify scientific problems that can benefit from machine learning techniques. Collaboration usually involves regular meetings to translate complex scientific challenges into data-driven models, sharing domain knowledge, and iteratively refining solutions. Effective communication and a willingness to bridge gaps between computational and scientific perspectives are essential. This interdisciplinary teamwork not only enhances the impact of AI solutions but also fosters ongoing learning and innovation.

Which 3 jobs will survive AI?

For roles related to AI for science, jobs such as research scientists, data analysts, and laboratory technicians are likely to persist as they require specialized knowledge, critical thinking, and hands-on experimentation that AI cannot fully replicate. These positions often involve complex problem-solving, interpretation of experimental data, and domain-specific expertise. Continuous learning and proficiency with AI tools can enhance job security in these fields.

What is AI for Science?

AI for Science refers to the application of artificial intelligence and machine learning techniques to accelerate scientific discovery and research. By leveraging large datasets, complex models, and advanced computational methods, AI helps scientists analyze data, identify patterns, simulate experiments, and make predictions across various scientific fields such as biology, chemistry, physics, and climate science. This approach can significantly speed up research, uncover new insights, and solve problems that were previously too complex or time-consuming for traditional methods.

What AI can I use for science?

AI for science involves using machine learning models, data analysis tools, and neural networks to analyze scientific data, make predictions, and automate research tasks. Common tools include TensorFlow, PyTorch, and specialized platforms like DeepMind or IBM Watson, often requiring programming skills in Python and knowledge of data science. These AI applications support fields such as biology, chemistry, physics, and environmental science.

Can AI take over science jobs?

AI for science roles involves automating data analysis, modeling, and research tasks, which can enhance productivity but are unlikely to fully replace scientists. Human expertise is essential for designing experiments, interpreting results, and making complex decisions. AI tools are typically used to support scientists rather than replace them entirely.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior AI researcher, machine learning director, or AI executive, often requiring advanced skills, extensive experience, and leadership responsibilities. These roles may involve overseeing AI projects, developing innovative algorithms, and working with cutting-edge tools, and they usually offer compensation in the upper echelons of the industry. Such salaries are more common in large tech companies or specialized AI firms.

What are the key skills and qualifications needed to thrive as an AI for Science Specialist, and why are they important?

To thrive as an AI for Science Specialist, you need a strong background in computer science, mathematics, and scientific domains, often supported by advanced degrees (e.g., PhD or MSc) in relevant fields. Proficiency with machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools, and familiarity with high-performance computing environments are typically required. Critical thinking, interdisciplinary collaboration, and effective communication are crucial soft skills for translating scientific problems into AI solutions. These skills are vital for developing innovative models, ensuring research rigor, and enabling impactful scientific discoveries.

What is the difference between Ai For Science vs Data Scientist?

AspectAi For ScienceData Scientist
Required CredentialsDegree in Science, Computer Science, or related fields; knowledge of AI and machine learningDegree in Statistics, Computer Science, or related fields; strong programming skills
Work EnvironmentResearch labs, scientific institutions, tech companies focused on scientific applicationsCorporate, tech firms, finance, healthcare, and other industries analyzing data
Industry UsageApplied to scientific research, simulations, and experimental data analysisUsed for data analysis, predictive modeling, and business insights

Ai For Science focuses on applying AI techniques to scientific research and experiments, often requiring a background in science and specialized knowledge of AI. Data Scientists analyze large datasets across various industries to extract insights and build models. While both roles involve AI and data analysis, Ai For Science is more research-oriented within scientific contexts, whereas Data Scientists work across diverse sectors on data-driven decision making.

What are popular job titles related to Ai For Science jobs in Wisconsin? For Ai For Science jobs in Wisconsin, the most frequently searched job titles are:
What job categories do people searching Ai For Science jobs in Wisconsin look for? The top searched job categories for Ai For Science jobs in Wisconsin are:
What cities in Wisconsin are hiring for Ai For Science jobs? Cities in Wisconsin with the most Ai For Science job openings:
Ai For Science jobs near you
Infographic showing various Ai For Science job openings in Wisconsin as of July 2026, with employment types broken down into 70% Full Time, 14% Part Time, and 16% Contract. Highlights an 87% In-person, and 13% Remote job distribution.
AI Engineer

AI Engineer

Farm Systems

Watertown, WI

Full-time

Posted yesterday

Job description

VAS is seeking an AI Engineer to lead building of scalable real-time production grade applications that use AI/ML models. This is a strategic, hands-on position for an experienced technical leader who has a track record of shipping AI-enhanced customer applications and tooling used by engineering teams.


This role will be focused on embedding LLMs, agent-based systems, and automation into core development workflows-boosting productivity, reducing manual toil, and accelerating delivery, essentially transforming how our engineers build, test, and ship software.

Responsibilities

  • Understand customer challenges and how integrating AI capabilities can help lead to solutions that have AI as a differentiator. 
  • Identify opportunities to apply AI for efficiency, growth, and customer value.
  • Drive awareness of AI capabilities and demonstrate how it can address customer needs, improve efficiency, reduce costs, and drive growth.
  • Drive transformation from AI-Ad Hoc to AI-Native engineering practices.
  • Serve as the AI technical SME, conduct R&D (research and development) to meet the needs of our AI strategy.
  • Continuously assess emerging AI tools and make data-driven recommendations.
  • Measure & Accelerate Adoption: Establish KPIs, track progress from the current to 100% adoption, implement interventions to accelerate uptake and communicate impact.
  • Build Center of Excellence: Create forums for knowledge sharing, celebrate wins, and foster peer-to-peer learning.
  • Establish AI governance frameworks and guardrails covering compliance, security, privacy, and ethical AI practices, and embed them into development workflows.
  • LLM Agents & Prompt Engineering
    • Architect and implement LLM agents.
    • Build composable, tool-augmented reasoning chains (e.g., RAG, CoT, ReAct, planner-executor).
    • Integrate vector databases and knowledge graphs to support retrieval-augmented generation (RAG).
    • Design and maintain high-quality prompt strategies for robustness and reliability.
  • Model Context Protocol (MCP) & Backend
    • Develop and maintain scalable APIs, supporting synchronous and asynchronous agent execution.
    • Integrate Model Context Protocol (MCP) to enable secure and structured access to external data and tools within agent workflows.
    • Implement state tracking, context-aware input dispatch, and modular plugin integration within the control plane.
  • Evaluation, Testing & Observability
    • Build unit and behavioral tests for agents, tools, and workflows.
    • Develop tooling for trace analysis, agent state debugging, and hallucination tracking.
    • Compare and benchmark agent orchestration frameworks for trade-offs in speed, reliability, and usability.
  • Model Fine-Tuning & MLOps
    • Integrate, deploy, fine tune and monitor models in production using cloud providers.
    • Set up agent logging, observability dashboards, and recovery workflows.
  • Front-end & User Experience
    • Collaborate with front-end developers or build user-facing components using React, TypeScript.
    • Ensure seamless user and agent interaction via UI and API bridges.

Education & Experience Requirements

  • Bachelor of Science in Software Engineering, Computer Science, Data Science, AI/ML or related field preferred.
  • 10+ years of experience in software development and design.
  • Proven experience in AI/ML solution design and hands experience with AI-powered development tools. 3+ years of experience preferred. 
  • Strong knowledge of Large Language Models, Generative AI, NLP, and Machine Learning concepts. (3+ years of experience preferred).
  • Hands-on experience with deep learning frameworks. 
  • Experience with RAG pipelines, vector databases, and agentic frameworks.
  • Familiarity with cloud-based AI services.

For the past 40 years we've woken up each day to support those that never stop feeding the world - and we have no plans to quit. We set the standard for farm management solutions and fix our eyes on raising the bar to meet the next generation of expectations.

Our software and information solutions help collect and connect a farm's data - from herd management to feed performance, tracking and more. These insights are a source of truth, empowering producers and their trusted advisors to make profit-driven and sustainable management decisions.

Whether near or far, large or small, VAS is at the heart of your dairy.

VAS has deep roots in the industry through its origin within the URUS family of companies. As a holding company with cooperative and private ownership, URUS is a family of businesses at the heart of the dairy and beef industry - Alta Genetics, GENEX, Genetics Australia, Leachman Cattle, Jetstream, PEAK, SCCL, Trans Ova Genetics and VAS.  Each organization has its unique identity, products, and services. These companies work globally to provide cutting-edge dairy and beef genetics, customized reproductive services to maximize conceptions, dairy management information to take producers to the frontline of progressive dairy farming, and an array of products and services to help bovines reach their full genetic potential. URUS has 9 brands in 17 retail countries and employs nearly 2,800 people globally.

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