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Machine Learning Engineer Jobs in Bend, OR (NOW HIRING)

The Strategic Innovation Engineer at BASX plays a critical role in the Strategic Development & Testing Group with researching, designing, developing, and implementing new and emerging technologies ...

As a Lead Building Engineer, you will provide senior-level technical leadership across mechanical ... Please note that Meta may leverage artificial intelligence and machine learning technologies in ...

Senior SCADA Engineer, EPC (Remote)

Bend, OR · On-site +1

$110K - $151K/yr

The Senior SCADA Engineer, EPC will lead and execute SCADA and PPC engineering workstreams for the design, development, and delivery of the SOLV OTS SCADA solution on utility-scale PV and BESS ...

Senior SCADA Engineer, EPC (Remote)

Bend, OR · On-site +1

$110K - $151K/yr

The Senior SCADA Engineer, EPC will lead and execute SCADA and PPC engineering workstreams for the design, development, and delivery of the SOLV OTS SCADA solution on utilityscale PV and BESS ...

Lead Building Engineer

Prineville, OR · On-site

$103K - $135K/yr

Meta is seeking a Lead Building Engineer to join our Data Center Critical Facility Operations team. Our data centers are the backbone of the infrastruct.

Qualifications: * 2+ years of hands-on experience in a cybersecurity role -- such as penetration testing, red teaming, incident response, detection engineering, DFIR, malware analysis, threat ...

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Showing results 1-20

Machine Learning Engineer information

See Bend, OR salary details

$33.2K

$135.9K

$204.1K

How much do machine learning engineer jobs pay per year?

As of Jul 12, 2026, the average yearly pay for machine learning engineer in Bend, OR is $135,851.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,100.00 and $163,500.00 per year, depending on experience, location, and employer.

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-demand industries or companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially in tech giants 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 AI advances, as they develop and refine algorithms, models, and systems. Roles that require complex problem-solving, creativity, and domain expertise—such as healthcare professionals, data scientists, software developers, cybersecurity specialists, and AI ethics officers—are also expected to persist due to their reliance on human judgment and specialized knowledge. These jobs often involve skills that are difficult for AI to fully replicate or replace.

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 engineers make $300,000 a year?

Senior machine learning engineers and data scientists with extensive experience, advanced skills in deep learning, and proficiency with tools like TensorFlow or PyTorch can earn $300,000 or more annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their expertise and impact on business outcomes.

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 job categories do people searching Machine Learning Engineer jobs in Bend, OR look for? The top searched job categories for Machine Learning Engineer jobs in Bend, OR are:
What cities near Bend, OR are hiring for Machine Learning Engineer jobs? Cities near Bend, OR with the most Machine Learning Engineer job openings:
Infographic showing various Machine Learning Engineer job openings in Bend, OR as of July 2026, with employment types broken down into 74% Full Time, 13% Part Time, and 13% Contract. Highlights an 50% In-person, 25% Hybrid, and 25% Remote job distribution, with an average salary of $135,851 per year, or $65.3 per hour.

$125K/yr

Other

Posted 11 days ago


Job description

WHAT IS DATA AND ANALYTICS (DA)-RESEARCH APPLIED ANALYTICS & STATISTICS (RAAS)?

A description of the business units can be found at: https://www.jobs.irs.gov/about/who/business-divisions

  • Position(s) are to be filled in the following area(s):
    • DAO DATA AND ANALYTICS
  • Consider each location carefully when applying. If you are selected for a location, that location will become your official post of duty.
REVIEW THE ADDITIONAL INFORMATION BELOW FOR FURTHER DETAILSQualifications:Federal experience is not required. Experience may have been gained in the public sector, private sector or through Volunteer Service. One year of experience refers to full-time work; part-timework is considered on a prorated basis. To ensure full credit for your work experience, please indicate dates of employment by month/day/year, and indicate number of hours worked per week, on your resume.
You must meet the following requirements by the cut-off dates as shown in announcement under the 'How to Apply' section.
QUALIFICATION REQUIRMENTS: BASIC REQUIREMENTS All GRADES: EDUCATION:
You must have a bachelor's or higher degree in mathematics, statistics, computer science, data science or other field directly related to the position. The degree must be in a major field of study (at least at the baccalaureate level) that is appropriate for the position.
OR
COMBINATION OF EDUCATION AND EXPERIENCE: A combination of education and experience that includes courses equivalent to a major field of study (30 semester hours) as shown in the paragraph above, plus additional education or appropriate experience.
AND
SPECIALIZED EXPERIENCE GRADE 14: In addition to the basic requirements, you must have one (1) year of specialized experience at a level of difficulty and responsibility equivalent to the GS-13 grade level in the Federal service. Specialized experience for this position includes experience performing all the following:
  • Leading data science or statistical analysis initiatives by defining project scope, analytic approach, data requirements, schedules, deliverables, or success measures; coordinating work across data, program, business, or technology stakeholders; and developing findings or recommendations for program or operational decisions.
  • Developing or applying statistical, machine learning, operations research, artificial intelligence, or other data science methods to evaluate programs, operations, compliance, or organizational performance, for example forecasting, predictive or prescriptive modeling, optimization, natural language processing or text analytics, graph or link analysis, neural networks or deep learning, or exploratory data analysis.
  • Overseeing data preparation, data quality, data governance, data certification, or analytic product delivery using programming, query, scripting, or analytic tools, such as Structured Query Language (SQL), R, Python, SAS, or equivalent tools, to support reproducible analysis, reporting, modeling, or decision-support products.
  • Experience manipulating datasets in relational databases (e.g., Compliance Data Warehouse, Enterprise Data Platform).
  • Advising managers or senior leaders on data science findings, automation opportunities, policy or program impacts, resource implications, risks, or recommended changes to processes, procedures, or operations.
  • Providing technical guidance, review, or mentoring to analysts or data scientists and preparing technical reports, briefings, presentations, or documentation that explain methods, assumptions, limitations, validation results, success measures, key performance indicators, or recommendations.
AND
You must also meet the following requirements:
  • MINIMUM AGE REQUIREMENT: Minimum age for federal employment is 18 years old, or at least 16 years old and have:
    • Graduated from high school or been awarded a certificate equivalent to graduating from high school; or
    • Completed a formal vocational training program; or
    • Received a statement from school authorities agreeing with your preference for employment rather than continuing your education
For more information on qualifications please refer to OPM's Qualifications Standards.Education:A college or university degree generally must be from an accredited (or pre-accredited) college or university recognized by the U.S. Department of Education. For a list of schools which meet these criteria, please refer to Department of Education Accreditation page.
FOREIGN EDUCATION: Education completed in foreign colleges or universities may be used to meet the requirements. You must show proof the education credentials have been deemed to be at least equivalent to that gained in conventional U.S. education program. It is your responsibility to provide such evidence when applying. Click here (Section 3, Explanation of Terms) or here for Foreign Education Credentialing instructions.
We recommend choosing an evaluator from a member organization of one of the following national associations of credential evaluation services: National Association of Credential Evaluation Services (NACES) or Association of International Credentials Evaluators (AICE).Employment Type: OTHER