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Senior Machine Learning Ops Engineer Jobs in Montana

As a Machine Learning Engineer, you will prepare datasets, train and optimize models, and maintain ... senior guidance * Excellent understanding of model evaluation techniques, feature engineering ...

... of senior machine operators and supervisors, learning more advanced machine operations and troubleshooting techniques. Experience/Education: • High School Diploma or equivalent. • Less than 1 ...

Work under the guidance of senior machine operators and supervisors, learning more advanced machine operations and troubleshooting techniques. Experience/Education * High School Diploma or equivalent.

... of senior machine operators and supervisors, learning more advanced machine operations and troubleshooting techniques. Experience/Education: • High School Diploma or equivalent. • Less than 1 ...

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Senior Machine Learning Ops Engineer information

What are the key skills and qualifications needed to thrive as a Senior Machine Learning Ops Engineer, and why are they important?

To thrive as a Senior Machine Learning Ops Engineer, you need expertise in machine learning, software engineering, cloud platforms, and experience with CI/CD pipelines, often supported by a computer science degree or equivalent experience. Proficiency with tools like Docker, Kubernetes, TensorFlow, PyTorch, and cloud services such as AWS, GCP, or Azure is typically required, along with familiarity with MLOps frameworks. Strong problem-solving, collaboration, and communication skills help you work effectively with cross-functional teams and manage complex ML model deployments. These skills are essential to ensure reliable, scalable, and efficient deployment of machine learning models in production environments.

What are some common challenges faced by Senior Machine Learning Ops Engineers when deploying models to production?

Senior Machine Learning Ops Engineers often encounter challenges such as ensuring model reproducibility, managing model versioning, and automating deployment pipelines for scalability. Another key challenge is monitoring model performance and data drift in production, which requires robust logging and alerting systems. Collaborating closely with data scientists, software engineers, and IT teams is essential to address these challenges and maintain a stable, efficient ML infrastructure.

What is the difference between Senior Machine Learning Ops Engineer vs Data Engineer?

AspectSenior Machine Learning Ops EngineerData Engineer
CredentialsExperience with ML frameworks, cloud platforms, scripting, and DevOps toolsStrong SQL, ETL, database, and programming skills, often with cloud experience
Work EnvironmentFocus on deploying, monitoring, and maintaining ML models in productionDesigning and building data pipelines and infrastructure for data processing
Industry UsageCommon in AI/ML-focused companies, tech firms, and data-driven organizationsWidespread across industries for data management and analytics

While both roles involve working with data and cloud platforms, the Senior Machine Learning Ops Engineer specializes in deploying and maintaining machine learning models, whereas the Data Engineer focuses on building data pipelines and infrastructure. Understanding these distinctions helps in choosing the right career path or job search focus.

What are Senior Machine Learning Ops Engineers?

Senior Machine Learning Ops (MLOps) Engineers are experienced professionals who design, build, and maintain the infrastructure and tools needed to deploy, monitor, and scale machine learning models in production environments. They work at the intersection of data science, software engineering, and DevOps to ensure ML models are robust, reliable, and secure. Their responsibilities often include automating model training pipelines, managing cloud resources, implementing CI/CD for ML, and ensuring model reproducibility. Senior MLOps Engineers also mentor junior staff and help define best practices for the organization’s ML workflow.
What are popular job titles related to Senior Machine Learning Ops Engineer jobs in Montana? For Senior Machine Learning Ops Engineer jobs in Montana, the most frequently searched job titles are:
What job categories do people searching Senior Machine Learning Ops Engineer jobs in Montana look for? The top searched job categories for Senior Machine Learning Ops Engineer jobs in Montana are:
What cities in Montana are hiring for Senior Machine Learning Ops Engineer jobs? Cities in Montana with the most Senior Machine Learning Ops Engineer job openings:
Infographic showing various Senior Machine Learning Ops Engineer job openings in Montana as of June 2026, with employment types broken down into 37% Full Time, 37% Part Time, 7% Temporary, 15% Contract, and 4% Nights. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution.
Machine Learning Engineer

Machine Learning Engineer

Indeed

Helena, MT

Other

Medical, PTO

Posted 18 hours ago


Indeed rating

9.5

Company rating: 9.5 out of 10

Based on 6 frontline employees who took The Breakroom Quiz

7th of 190 rated software companies


Job description

Our Mission

Our Mission

As the world’s number 1 job site*, our mission is to help people get jobs. We strive to cultivate an inclusive and accessible workplace where all people feel comfortable being themselves. We're looking to grow our teams with more people who share our enthusiasm for innovation and creating the best experience for job seekers.

(*Comscore, Total Visits, March 2025)

Day to Day

The Machine Learning Engineer I role partners closely with business partners across various functions to help execute strategic initiatives that increase revenue, drive operational scale, and improve efficiency for continuous growth. As a Machine Learning Engineer, you will prepare datasets, train and optimize models, and maintain and improve model inference services. You will learn and apply new techniques from open source packages and research publications, and creatively adapt models for solving business problems across Indeed.

Work spans classical ML through LLM systems. You improve search and retrieval quality using real user signals. Execution includes experiments, iteration, and production reliability at scale. You collaborate with engineers, data scientists, and product teams to define problems, test approaches, and ship measurable improvements.

Responsibilities

  • Build AI/ML systems for search, ranking, and recommendations

  • Develop LLM retrieval and generation workflows

  • Improve search and ranking relevance

  • Design metrics and run experiments

  • Monitor model quality, latency, and cost

  • Debug data, models, and system issues

  • Build training, inference, and eval pipelines

Skills/Competencies

  • Requires a Bachelor's degree in Computer Science, Mathematics, or Statistics, and a minimum of 2 years of related experience; or an advanced degree without experience

  • Experience building ML models in Python; solid software engineering and algorithms fundamentals

  • Experience developing backend services in Java/Kotlin for ML-driven systems and features

  • Experience writing clean, testable, and maintainable production code

  • Experience working with structured and unstructured data, including SQL for large-scale data querying, and building scalable data pipelines and features from data

  • Experience integrating ML models into search systems using engines such as OpenSearch or similar, with familiarity in container orchestration for deployment with senior guidance

  • Excellent understanding of model evaluation techniques, feature engineering, experiment design, and familiarity with LLM systems (RAG, embeddings, output evaluation)

Salary Range Transparency

Tier 1 - United States of America 118,000 - 176,000 USD per year

Tier 2 - United States of America 130,000 - 196,000 USD per year

Tier 3 - United States of America 143,000 - 215,000 USD per year

Tier 4 - N/A

Tier 5 - United States of America 163,000 - 245,000 USD per year

Salary Range Disclaimer

The salary range for this role reflects the minimum and maximum compensation for the role. Offers are typically made between the range minimum and the range midpoint. Actual compensation will be determined based on job-related skills, experience, and expertise, as evaluated during the interview process. The range(s) listed is just one component of Indeed's total compensation package for employees. Other rewards may include quarterly bonuses, Restricted Stock Units (RSUs), a Paid Time Off policy, and many region-specific benefits. Compensation may also vary based on where a role is performed, as work locations are grouped into geographic pay tiers to reflect cost of labor differences in different geographic markets. Candidates can view geographic pay tiers by location on our career site (https://www.indeed.com/careers/paytiers), and recruiters can confirm how location is considered for a specific role.

Benefits - Health, Work/Life Harmony, & Wellbeing

Indeed is deeply committed to building a workplace and global community where inclusion is not only valued, but prioritized. We’re proud to be an equal opportunity employer, seeking to create a welcoming and diverse environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, family status, marital status, sexual orientation, national origin, genetics, neuro-diversity, disability, age, or veteran status, or any other non-merit based or legally protected grounds.

Indeed provides reasonable accommodations to qualified individuals with disabilities in the employment application process. To request an accommodation, please visit https://www.indeed.com/careers/accommodations. If you are requesting accommodation for an interview, please reach out at least one week in advance of your interview.

For more information about our commitment to equal opportunity/affirmative action, please visit our Careers page (https://www.indeed.com/careers).

Equal Opportunities and Accommodations Statement

Inclusion and belonging are fundamental to our hiring practices and company culture, forming an integral part of our vision for a better world of work. At Indeed, we’re committed to the wellbeing of our employees and on a mission to make this the best place to work and thrive. We believe that fostering an inclusive environment where every employee feels respected and accepted benefits everyone, fueling innovation and creativity.

We value diverse experiences, including those who have had prior contact with the criminal legal system. We are committed to providing individuals with criminal records, including formerly incarcerated individuals, a fair chance at employment.

Those with military experience are encouraged to apply. Equivalent expertise demonstrated through a combination of work experience, training, military experience, or education is welcome.

Indeed’s Employee Recruiting Privacy Policy

Like other employers Indeed uses our own technologies to help us find and attract top talent from around the world. In addition to our site’s user and privacy policy found at https://www.indeed.com/legal, we also want to make you aware of our recruitment specific privacy policy found at https://www.indeed.com/legal/indeed-jobs.

Agency Disclaimer

Indeed does not pay placement fees for unsolicited resumes or referrals from non-candidates, including search firms, staffing agencies, professional recruiters, fee-based referral services, and recruiting agencies (each individually, an "Agency"), subject to local laws. An Agency seeking a placement fee must obtain advance written approval from Indeed's internal Talent Acquisition team and execute a fee agreement with Indeed for each job opening before making a referral or submitting a resume for that opening.

AI Notice

Indeed is committed to ensuring fairness and transparency throughout our hiring process. We use artificial intelligence (AI) tools to assist in the screening, assessment, and selection of applicants for this position by analyzing information provided in resumes and applications. Our use of AI does not replace human decision-making.

Unless otherwise notified, Indeed does not use AI constituting an AEDT or an ADMT as those tools are defined in applicable laws.

The deadline to apply to this position is 6/12/2026. Job postings may be extended at the hiring team’s discretion based on applicant volume.

Reference ID: 46644

Reference ID: 46644