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Machine Learning Engineer Jobs in Montana (NOW HIRING)

Technical Skills & Knowledge Strong understanding of AI and machine learning concepts, including ... Bachelor's degree in computer science, Engineering, Data Science, Applied Mathematics, or a related ...

AI Strategist

Missoula, MT ยท On-site

$120K - $140K/yr

Technology Support โ€ข Stay current on advancements in AI, Machine Learning, NLP, LLMs, GenAI, and ... Education: โ€ข Bachelor's degree in computer science, Engineering, Data Science, Applied ...

Insights from Data (Big Data, Machine Learning etc.) * Infrastructure as Code adoption (Terraform ... CEO to the developer. * Assigned to a number of accounts and are expected to work with their ...

$50/hr

Strong analytical and programming skills in deep learning using frameworks and tools for machine learning (e.g., PyTorch ) and visualization (e.g., TensorBoard ). * Experience in research communities ...

$50/hr

Strong analytical and programming skills in deep learning using frameworks and tools for machine learning (e.g., PyTorch ) and visualization (e.g., TensorBoard ). * Experience in research communities ...

$50/hr

Strong analytical and programming skills in deep learning using frameworks and tools for machine learning (e.g., PyTorch ) and visualization (e.g., TensorBoard ). * Experience in research communities ...

Showing results 41-60

Machine Learning Engineer information

See Montana salary details

$28.9K

$118.2K

$177.6K

How much do machine learning engineer jobs pay per year?

As of Aug 20, 2026, the average yearly pay for machine learning engineer in Montana is $118,190.00, according to ZipRecruiter salary data. Most workers in this role earn between $93,200.00 and $142,300.00 per year, depending on experience, location, and employer.

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What does a machine learning engineer do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

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

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What are some common challenges faced by machine learning engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

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

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

What are the most commonly searched types of Machine Learning Engineer jobs in Montana?

The most popular types of Machine Learning Engineer jobs in Montana are:

What are popular job titles related to Machine Learning Engineer jobs in Montana?

For Machine Learning Engineer jobs in Montana, the most frequently searched job titles are:

What job categories do people searching Machine Learning Engineer jobs in Montana look for?

The top searched job categories for Machine Learning Engineer jobs in Montana are:

What cities in Montana are hiring for Machine Learning Engineer jobs?

Cities in Montana with the most Machine Learning Engineer job openings:

What are popular job titles related to Machine Learning Engineer jobs in MT?

For Machine Learning Engineer jobs in MT, the most frequently searched job titles are:

Infographic showing various Machine Learning Engineer job openings in Montana as of August 2026, with employment types broken down into 67% Full Time, and 33% Part Time. Highlights an 100% In-person job distribution, with an average salary of $118,190 per year, or $56.8 per hour.

Staff Application Security Engineer - AI & Agentic Systems

Hispanic Alliance for Career Enhancement

Helena, MT โ€ข On-site

$107 - $284/hr

Other

Medical, Dental, Vision, Retirement, PTO

Posted 2 days ago

New


Job description

We're building a world of health around every individual - shaping a more connected, convenient and compassionate health experience. At CVS Healthยฎ, you'll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger - helping to simplify health care one person, one family and one community at a time.

Position Summary

We are seeking a Staff Application Security Engineer - AI & Agentic Systems to help secure the next generation of AI-enabled applications, large language model integrations, and agentic systems. This role will partner closely with engineering, product, platform, and data teams to embed security into the design, development, and operation of both traditional and AI-driven solutions.

As a senior technical contributor, you will help define secure design patterns, conduct threat modeling and risk assessments, guide engineering teams on secure development practices, and support the adoption of responsible AI security controls across the enterprise.

Key Responsibilities Secure Development, Standards & Design
  • Develop and maintain application and AI security standards, best practices, and guardrails.
  • Promote secure-by-design principles across application and AI development lifecycles.
  • Establish secure design patterns for AI agents, prompt management, tool integrations, memory handling, and autonomous workflows.
  • Partner with engineering teams to identify and mitigate AI-specific security risks such as prompt injection, model abuse, data leakage, and unauthorized agent actions.
AI & Agentic Security Architecture
  • Serve as a subject matter expert supporting the security of AI-enabled applications and agentic systems.
  • Review and provide guidance on architectures utilizing large language models, retrieval augmented generation pipelines, AI agents, and AI-powered workflows.
  • Define and recommend identity, authorization, data protection, observability, and governance controls for AI environments.
  • Collaborate with AI platform, engineering, product, and data teams to ensure secure and responsible AI adoption.
Security Testing & Risk Management
  • Perform threat modeling, architecture reviews, and security assessments for applications and AI-enabled systems.
  • Conduct security analysis of AI workflows, model integrations, agent interactions, and data flows.
  • Partner with engineering teams to prioritize and remediate security findings.
  • Evaluate emerging AI security tools and technologies to improve organizational security posture.
Collaboration & Technical Leadership
  • Influence engineering teams to integrate security controls into development processes and product roadmaps.
  • Partner with privacy, compliance, legal, and risk teams to align security controls with organizational requirements.
  • Provide guidance on AI security considerations, emerging threats, and industry best practices.
  • Participate in strategic initiatives supporting secure AI adoption across the enterprise.
Incident Response & Continuous Improvement
  • Support investigation and response efforts involving application and AI-related security events.
  • Assist in identifying root causes and implementing long-term security improvements.
  • Drive continuous improvement of security controls, processes, and engineering practices.
Mentorship & Innovation
  • Mentor security engineers and development teams on secure coding, threat modeling, and AI security best practices.
  • Contribute to the evaluation of emerging AI security research, technologies, and industry standards.
  • Help advance the organization's application and AI security strategy through innovation and technical leadership.
Required Qualifications
  • 7+ years of experience designing, building, or securing enterprise-scale applications and platforms.
  • 5+ years of application security experience, including threat modeling, secure design, code review, and vulnerability management.
  • 5+ years of programming experience in one or more languages such as Python, Java, JavaScript, C#, or Go.
  • 3+ years of experience developing or securing AI, machine learning, or generative AI solutions.
  • 3+ years of experience with public cloud platforms including AWS, Azure, or Google Cloud Platform.
Preferred Qualifications
  • Experience securing modern application architectures, including APIs, containers, microservices, and serverless technologies.
  • Strong understanding of secure software development lifecycle practices and application security testing methodologies.
  • Handsโ€‘on experience securing AI agents, retrieval augmented generation solutions, and large language model integrations.
  • Experience conducting threat modeling and security assessments for AI-enabled systems.
  • Familiarity with responsible AI principles, AI governance, and emerging AI security frameworks.
  • Experience integrating security controls into continuous integration and continuous delivery pipelines.
  • Knowledge of common compliance frameworks such as PCI DSS, HIPAA, NIST, HITRUST, and CSA.
  • Ability to influence technical decisions across multiple teams and organizations.
  • Experience contributing to security research, openโ€‘source projects, or industry communities.
Education
  • Bachelor's degree from an accredited college or university, or equivalent combination of education and relevant work experience (High School Diploma/GED plus 4 years of related experience)

Health100 is America's trusted front door to health and care. The Health100 platform integrates any participating health plan, PBM, pharmacy (retail and specialty), provider, digital health point solution provider, and employer, and addresses the top health care challenges for the consumer.

Pay Range

The typical pay range for this role is:

$106,605.00 - $284,280.00

This pay range represents the base hourly rate or base annual fullโ€‘time salary for all positions in the job grade within which this position falls. The actual base salary offer will depend on a variety of factors including experience, education, geography and other relevant factors. This position is eligible for a CVS Health bonus, commission or shortโ€‘term incentive program in addition to the base pay range listed above. This position also includes an award target in the company's equity award program.

Our people fuel our future. Our teams reflect the customers, patients, members and communities we serve and we are committed to fostering a workplace where every colleague feels valued and that they belong.

Great benefits for great people

We take pride in offering a comprehensive and competitive mix of pay and benefits that reflects our commitment to our colleagues and their families.

This fullโ€‘time position is eligible for a comprehensive benefits package designed to support the physical, emotional, and financial wellโ€‘being of colleagues and their families. The benefits for this position include medical, dental, and vision coverage, paid time off, retirement savings options, wellness programs, and other resources, based on eligibility.

Additional details about available benefits are provided during the application process and on Benefits Moments.

We anticipate the application window for this opening will close on: 08/24/2026

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.

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