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Machine Learning Engineer Jobs in Montana (NOW HIRING)

... machine learning, and translate emerging capabilities into actionable product opportunities. Qualifications : Required : • Bachelor's degree in computer science, engineering, data science, or a ...

... machine learning, and translate emerging capabilities into actionable product opportunities. Qualifications : Required : • Bachelor's degree in computer science, engineering, data science, or a ...

... machine learning, and translate emerging capabilities into actionable product opportunities. Qualifications : Required : • Bachelor's degree in computer science, engineering, data science, or a ...

You will work with data engineering, analytics, machine-learning, and data science teams to deliver initiatives that support the enterprise's data needs. You will function as a TPM authority and team ...

Senior Director, Product Content Operations

Missoula, MT · On-site

$125K - $165K/yr

... and machine learning tools to improve efficiency, data quality, and operational scalability. Enable new product experiences through content * Collaborate with Product and Engineering leaders to ...

Senior AI Product Manager

Bozeman, MT · On-site +1

$136K - $180K/yr

... of machine learning and artificial intelligence capabilities within Workiva's platform. In this ... You will collaborate directly with engineering, data science, and design teams to deliver high ...

... machine learning, and translate emerging capabilities into actionable product opportunities. Requirements: * Bachelor's degree in computer science, engineering, data science, or a related field ...

... machine learning, and translate emerging capabilities into actionable product opportunities. Requirements: * Bachelor's degree in computer science, engineering, data science, or a related field ...

Senior Director, Product Content Operations

Missoula, MT · On-site

$125K - $165K/yr

... and machine learning tools to improve efficiency, data quality, and operational scalability. Enable new product experiences through content * Collaborate with Product and Engineering leaders to ...

Senior AI Product Manager

Bozeman, MT · On-site +1

$129K - $170K/yr

... of machine learning and artificial intelligence capabilities within Workiva's platform. In this ... You will collaborate directly with engineering, data science, and design teams to deliver high ...

Senior AI Product Manager

Bozeman, MT · On-site +1

$129K - $170K/yr

... of machine learning and artificial intelligence capabilities within Workiva's platform. In this ... You will collaborate directly with engineering, data science, and design teams to deliver high ...

Senior Director, Product Content Operations

Missoula, MT · On-site

$230K - $241K/yr

... and machine learning tools to improve efficiency, data quality, and operational scalability. Enable new product experiences through content * Collaborate with Product and Engineering leaders to ...

Software Engineer III

Helena, MT · Hybrid

$116K - $153K/yr

For us, learning isn't just what we do. It's who we are. To learn more: We are Pearson. Pearson is an Equal Opportunity Employer and a member of E-Verify. Employment decisions are based on ...

Senior Software Engineer

Bozeman, MT

$125K - $164K/yr

Senior Software Engineer - Bozeman, MT Intertek, a leading provider of quality and safety solutions ... Design, build, and maintain clean, scalable backend APIs and services that power our learning ...

Senior NPI Engineer

Bozeman, MT · On-site

$106K - $146K/yr

The NPI Engineer is responsible for shepherding hardware from concept review to low-volume ... Proficient in SPC, 6 sigma, quality control process; good understanding of machine and process ...

Senior NPI Engineer

Bozeman, MT · On-site

$106K - $146K/yr

The NPI Engineer is responsible for shepherding hardware from concept review to low-volume ... Proficient in SPC, 6 sigma, quality control process; good understanding of machine and process ...

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Showing results 1-20

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 Jun 17, 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.

Is ML full of coding?

Machine Learning Engineers typically do a significant amount of coding, especially in languages like Python or R, to develop algorithms, preprocess data, and build models. Strong programming skills are essential, along with knowledge of frameworks such as TensorFlow or PyTorch, but the role also involves data analysis, model evaluation, and collaboration with teams. Coding is a core component of the job, though some tasks may involve model deployment and optimization that require different skills.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-paying industries such as finance or technology can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially at large tech companies or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

What are Machine Learning Engineers?

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 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.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as they develop, implement, and maintain AI systems, requiring specialized skills in programming, data analysis, and model optimization. Roles that involve complex problem-solving, creativity, and human interaction—such as healthcare professionals, educators, skilled tradespeople, and certain managerial positions—are also expected to persist despite AI advancements. These jobs typically require emotional intelligence, adaptability, and domain expertise that AI cannot easily replicate.

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 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 June 2026, with employment types broken down into 100% Full Time. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $118,190 per year, or $56.8 per hour.
Product Owner

Product Owner

Tactacam

Billings, MT • On-site

Full-time

Posted 21 days ago


Tactacam rating

9.2

Company rating: 9.2 out of 10

Based on 5 frontline employees who took The Breakroom Quiz


Job description

Job Summary:
Tactacam is a leading innovator in outdoor and action camera technology, dedicated to providing high-quality products that enhance the outdoor experience. They are seeking a Product Owner for AI Platforms & Vision Systems to lead the development of AI capabilities through strategic partnerships and drive their integration into products. The role involves managing the AI platform roadmap, collaborating with engineering teams, and defining scalable integration patterns for AI capabilities.
Responsibilities:
• Own the product roadmap for AI platform capabilities, including computer vision (image and video analysis, detection, classification, and scene understanding) and broader AI features such as language-based systems, multimodal experiences, and intelligent automation.
• Lead day-to-day collaboration with third-party AI partners (model providers, MLOps platforms, and edge/embedded solutions), including scoping, requirements, model evaluation, tuning, and acceptance.
• Define scalable integration patterns for incorporating AI capabilities into internal systems and customer-facing products, including APIs, data pipelines, inference workflows, and performance trade-offs (latency, cost, reliability).
• Partner with engineering teams across software, hardware, and firmware to design and deliver services and SDKs that enable AI-powered functionality across applications, devices, and internal tools.
• Collaborate with business stakeholders to identify high-impact opportunities for AI, balancing performance, cost, and user experience considerations.
• Write clear user stories, acceptance criteria, and technical specifications; manage backlog prioritization and lead agile ceremonies for the AI platform workstream.
• Define and track product and model performance metrics, including quality, accuracy, latency, cost efficiency, user adoption, and operational impact.
• Lead model evaluation efforts using curated datasets and evaluation frameworks, incorporating real-world edge cases and diverse operating conditions.
• Partner with data and engineering teams to ensure responsible data collection, governance, and usage, including privacy and compliance considerations.
• Collaborate with UX, Marketing, Customer Support, and Operations teams to shape how AI-driven capabilities are delivered and experienced, including feedback loops and continuous improvement mechanisms.
• Support vendor evaluation, selection, and ongoing management, including commercial considerations and service-level expectations.
• Stay current on advancements in AI, computer vision, and machine learning, and translate emerging capabilities into actionable product opportunities.
Qualifications:
Required:
• Bachelor’s degree in computer science, engineering, data science, or a related field (preferred but not required).
• 3–5 years of Product Owner (or similar product role) experience, including direct ownership of the roadmap, backlog, and delivery for technical products.
• At least 1 year of hands-on experience working on AI/ML-powered products, including computer vision and/or large language model use cases.
• Demonstrated experience shipping AI-driven features from concept to production, including defining requirements, working with engineering teams, and measuring outcomes post-launch.
• Practical experience working with large language models in a product setting, such as prompt design, retrieval-augmented generation (RAG), tool/function calling, or evaluation of model outputs.
• Experience working with or evaluating third-party AI vendors or foundation model providers, including comparing capabilities, performance, and cost trade-offs.
• Solid understanding of core AI/ML concepts, including the differences between training, fine-tuning, and prompting, as well as common evaluation metrics (e.g., precision/recall, accuracy, latency, cost-per-inference).
• Experience defining API contracts and integrating external services into applications, including handling reliability, fallbacks, and edge cases.
• Familiarity with model evaluation workflows, including use of labeled datasets, test cases, and structured evaluation approaches for both vision and language models.
• Strong written communication skills, with the ability to create clear product specs, user stories, and stakeholder updates for both technical and non-technical audiences.
• Strong analytical and problem-solving skills, with a focus on measurable outcomes and data-driven decision-making.
• Ability to operate cross-functionally and influence engineering, data, design, and business stakeholders.
Preferred:
• Hands-on experience with computer vision applications such as object detection, image classification, or video analysis in real-world environments.
• Experience building or integrating multimodal AI features that combine vision and language capabilities.
• Experience applying AI to improve customer experiences or internal operations (e.g., automation, support workflows, or decision support systems).
• Familiarity with MLOps tools, dataset curation, and data labeling workflows.
• Experience with edge or on-device inference, mobile AI integration, or IoT/device-based systems.
• Experience tuning model outputs (e.g., thresholds, prompts, business logic) to balance accuracy, user experience, and operational impact.
• Background in consumer technology, connected devices, or related product categories.
Company:
Tactacam sells hunting and outdoor cameras and accessories. Founded in 2013, the company is headquartered in Caledonia, USA, with a team of 201-500 employees. The company is currently Growth Stage.