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Machine Learning Engineer Opt Jobs in Colorado (NOW HIRING)

True Anomaly

Machine Learning Engineer (I-III)

Denver, CO · On-site

$105K - $220K/yr

Bachelor's degree in computer science, machine learning, data science, electrical engineering, or a similar discipline * Proficient in Python * Foundational understanding of machine learning concepts ...

Bespoke Labs

Machine Learning Engineer

Denver, CO · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Thornton, CO

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Greeley, CO

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Aurora, CO

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Workday

Machine Learning Engineer III

Boulder, CO · On-site

$136K - $240K/yr

We're forming small, senior, cross-functional AI teams that bring together product leaders, machine learning engineers, and full-stack builders to create intelligent agents used by millions of people ...

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Machine Learning Engineer Opt information

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models into production environments. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, reliable systems that organizations can use to make predictions or automate tasks. Their responsibilities include data preprocessing, choosing appropriate algorithms, model training, and ensuring the model's performance in real-world applications. Machine Learning Engineers often collaborate with data scientists, data engineers, and product teams to deliver intelligent solutions.

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

AspectMachine Learning Engineer OptData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; certifications in ML toolsBachelor's or Master's in CS, Statistics, or related fields; data analysis certifications
Work EnvironmentDevelops, tests, and deploys ML models in production systemsAnalyzes data, builds models, and provides insights for decision-making
Employer & Industry UsageTech companies, AI startups, e-commerce, financeResearch institutions, tech firms, consulting, finance
Common Search & ComparisonOften compared for technical skills and deployment focusCompared for data analysis and business insights

Machine Learning Engineers Opt focus on deploying scalable ML models in production environments, while Data Scientists primarily analyze data and develop models for insights. Both roles require strong technical skills, but their core responsibilities differ in application and deployment.

Is a machine learning engineer still in demand?

Yes, machine learning engineers are in high demand due to the growing adoption of AI and data-driven solutions across industries. They are sought after for their skills in programming, data analysis, and familiarity with tools like Python, TensorFlow, and cloud platforms, making this a strong career choice for those with relevant expertise.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as AI advances because they develop and refine AI models, requiring specialized skills in programming, data analysis, and domain knowledge. Jobs that involve complex problem-solving, creativity, and emotional intelligence, such as healthcare professionals, educators, and skilled tradespeople, are also expected to persist despite AI automation. Continuous learning and adapting to new tools and technologies will be essential for job security across many fields.

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 a solid background in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch), data processing tools, and cloud platforms, along with relevant certifications, is highly valuable. Strong problem-solving ability, collaboration, and effective communication are standout soft skills in this role. These skills and qualities ensure the successful development, deployment, and integration of machine learning solutions that drive business value.

What is a $900,000 AI job?

A $900,000 AI-related job typically refers to high-level roles such as senior machine learning engineers, AI research directors, or chief AI officers, often in large tech companies or specialized firms. These positions usually require advanced skills in machine learning, deep learning, and data science, along with extensive experience and leadership responsibilities.

What are some common challenges Machine Learning Engineers face when deploying models to production environments?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, handling data drift, and integrating models seamlessly with existing systems when deploying to production. Monitoring model performance in real time and retraining models as new data becomes available are also critical tasks. Collaboration with data engineers and DevOps teams is essential to address infrastructure and deployment hurdles while maintaining model accuracy and reliability.

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 tech, 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.
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Machine Learning Engineer Opt jobs near you
Senior Machine Learning Engineer

Senior Machine Learning Engineer

The Marlin Alliance

Denver, CO

$126K - $166K/yr

Other

Posted 13 days ago

Job description

Senior Machine Learning Engineer

The Marlin Alliance, Inc. is seeking a Senior Machine Learning Engineer to design, develop, and implement advanced machine learning models and algorithms in support of naval applications. This role requires deep technical expertise in modern machine learning methods, distributed systems, cloud-native development, and software engineering best practices. The Senior ML Engineer will collaborate with multidisciplinary teams to deliver mission-focused AI solutions that integrate into operational Navy environments.

Incorporated in 2002, The Marlin Alliance is a digital transformation company dedicated to ensuring our clients compete and win in tomorrow's digital world. We specialize in creating technical solutions that allow for seamless execution of automated business processes and the generation of governed, machine-consumable data. From strategic planning to advanced analytics and cybersecurity, our team provides cutting-edge, cross-disciplinary solutions. We are seeking motivated professionals who share our agile, solution-oriented mindset and are ready to deliver the real, practical results relied upon by our clients.

Citizenship and Clearance requirements:

  • U.S. Citizenship required
  • No dual citizenship
  • Active TS security clearance required
  • Active TS SCI security clearance preferred

Location - ON-Site near one of the following locations:

  1. 1st Space Brigade - Fort Carson, CO
  2. Air Force TENCAP - Colorado Springs, CO
  3. NIWC LANT - Charleston, SC
  4. Buckley Space Force Base - Denver, CO
  5. NAVWAR - San Diego, CA

Travel: 15%

Responsibilities:

  • Collaborate with cross-functional teams to understand and address Navy operational challenges using data pipelines and analytics.
  • Design, develop, and implement data pipelines and analytics for naval applications.
  • Perform exploratory data analysis, algorithm development, and testing.
  • Normalize and structure data to common standards for interoperability.
  • Work with multiple data formats, including CSV, JSON, XML, Parquet, and ORC.
  • Develop and deploy data pipelines and analytics in real-world operational environments.
  • Deploy, monitor, and optimize data pipelines to ensure high performance and reliability.
  • Implement event streaming pipelines using Apache Kafka, AWS Kinesis, RabbitMQ, or ZeroMQ.
  • Utilize distributed computing platforms such as AWS Lambda, Dask, or Spark.
  • Leverage cloud-native tools including AWS S3, RDS, EFS, SNS, and SQS.
  • Utilize data pipeline frameworks such as AirByte, Apache Airflow, dbt, Apache Iceberg, and Snowflake.
  • Work with GIS data using ArcGIS, PostGIS, and related tooling.
  • Implement containerized environments using Docker or Kubernetes.
  • Apply cybersecurity principles in the context of secure DoD data applications.
  • Communicate findings and engineering solutions effectively with technical and mission stakeholders.

Required Skills and Experience:

  • Experience as a data scientist, data engineer, geospatial engineer, machine learning engineer, or software engineer.
  • Proven experience developing and deploying algorithms, mathematical models, or machine learning models in real-world applications.
  • Strong programming skills in Python.
  • Familiarity with cloud platforms (e.g., AWS, Azure) or containerization technologies (e.g., Docker, Kubernetes).
  • Familiarity with software engineering best practices, including Git.
  • Strong programming skills in Java, C++, Go, or Rust.
  • Strong analytical, problem-solving, and communication skills.
  • Ability to work effectively in a collaborative team environment.
  • Ability to safely carry tools, equipment, and materials aboard ship, including ascending and descending shipboard ladders(stairwells) and navigating confined spaces while maintaining required points of contact. Tools and equipment will weigh no more than 50 lbs.
  • Ability to perform required work aboard Navy vessels and in shipboard environments, including navigating narrow passageways, ascending and descending ladders (stairwells), working on elevated platforms, and operating in variable sea conditions.
  • Ability to perform activities on a recurring basis during shipboard operations or testing evolutions.
  • Ability to comply with Navy safety requirements and wear required personal protective equipment (PPE).
  • Candidates should be prepared to complete a coding exercise as part of the interview process.

Preferred Skills and Experience:

  • Experience with distributed computing and parallel processing.
  • Experience with CI/CD pipelines and automation tools (GitHub Actions, GitLab CI, Jenkins).
  • Experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
  • Experience with cloud-native architecture and software API design.
  • Experience integrating machine learning into operational DoD systems or edge computing environments.
  • Familiarity with DoD AI strategies, MLOps, or data engineering in secure environments.
  • Previous experience supporting government agencies or military organizations. (NAVWAR, NIWC Pacific, or other Navy C2/ISR programs strongly preferred).

Education and Certification Requirements:

  • Bachelor of Science in Computer Science, Data Science, Geography, Math, Machine Learning, or Statistics, OR Equivalent years of relevant experience in lieu of a degree
  • Additional certifications in cloud, data engineering, GIS, or cybersecurity are a plus

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