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Junior Machine Learning Jobs in Denver, CO (NOW HIRING)

Bet365

Data Science Team Lead/Manager

Denver, CO · On-site

This is not a pure people-management role. • Building, mentoring, and guiding a pragmatic, delivery-focused team of Junior Data Scientists and Machine Learning Engineers, fostering a culture of ...

Bet365

Senior Data Scientist

Denver, CO · On-site

Building, mentoring, and guiding a pragmatic, delivery-focused team of Junior Data Scientists and Machine Learning Engineers, fostering a culture of rapid iteration, continuous learning, and software ...

CACI

Staff AI/ML Engineering Manager

Aurora, CO · On-site

... junior engineers, project leadership) 8+ years of experience developing AI/ML applications, data science, or algorithm development Experience with Python and data science / machine learning libraries ...

bet365

Data Science Team Leader

Denver, CO · On-site

Building, mentoring, and guiding a pragmatic, delivery-focused team of Junior Data Scientists and Machine Learning Engineers, fostering a culture of rapid iteration, continuous learning, and software ...

bet365

Data Science Team Leader

Denver, CO · On-site

$155K - $165K/yr

Building, mentoring, and guiding a pragmatic, delivery-focused team of Junior Data Scientists and Machine Learning Engineers, fostering a culture of rapid iteration, continuous learning, and software ...

bet365

Data Science Team Leader

Denver, CO · On-site

$155K - $165K/yr

Building, mentoring, and guiding a pragmatic, delivery-focused team of Junior Data Scientists and Machine Learning Engineers, fostering a culture of rapid iteration, continuous learning, and software ...

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Junior Machine Learning information

See Denver, CO salary details

$7

$27

$48

How much do junior machine learning jobs pay per hour?

As of Sep 3, 2026, the average hourly pay for junior machine learning in Denver, CO is $27.75, according to ZipRecruiter salary data. Most workers in this role earn between $16.83 and $34.13 per hour, depending on experience, location, and employer.

What does a junior machine learning engineer do?

A Junior Machine Learning Engineer assists in the development and implementation of machine learning models and algorithms under the supervision of more experienced engineers. They typically help with data collection, cleaning, feature engineering, model training, and evaluation. Junior engineers may also write code, test prototypes, and contribute to improving model performance while learning best practices in the field. Their role often involves collaborating with data scientists and software engineers to integrate machine learning solutions into products or services.

What are the key skills and qualifications needed to thrive as a junior machine learning engineer?

To thrive as a Junior Machine Learning Engineer, you need a solid understanding of programming (especially Python), basic statistics, linear algebra, and familiarity with machine learning concepts, typically supported by a relevant degree or coursework. Proficiency in tools and frameworks like scikit-learn, TensorFlow, PyTorch, and version control systems such as Git is often expected. Strong problem-solving abilities, curiosity, and effective communication are crucial soft skills for collaborating with teams and explaining technical concepts. These skills and qualities are important because they enable you to contribute effectively to building, testing, and improving machine learning models in real-world applications.

What types of projects and tasks can a junior machine learning professional typically expect to work on in their first year?

As a Junior Machine Learning professional, you’ll often support senior data scientists and engineers by preparing data, implementing basic algorithms, and assisting with model evaluation. Your daily tasks may include data cleaning, feature engineering, running experiments, and writing code to automate data pipelines. You might also help document processes and present your findings to team members. While the work is often collaborative, you’ll have opportunities to take ownership of smaller projects and progressively contribute to larger initiatives as you gain experience.

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

AspectJunior Machine LearningData Scientist
Required CredentialsBachelor's in CS, Data Science, or related field; some experience with ML toolsBachelor's or Master's in CS, Statistics, or related; strong programming and statistical skills
Work EnvironmentEntry-level projects, supervised tasks, team collaborationAdvanced analysis, model development, cross-functional teams
Industry UsageCommon in tech companies, startups, research labsWidespread across industries like finance, healthcare, tech

Junior Machine Learning roles focus on foundational ML tasks and learning on the job, while Data Scientists handle complex data analysis, model building, and strategic insights. The roles differ mainly in experience level and scope of responsibilities, but both require strong technical skills and familiarity with data tools.

What are the most commonly searched types of Machine Learning jobs in Denver, CO?

The most popular types of Machine Learning jobs in Denver, CO are:

What are popular job titles related to Junior Machine Learning jobs in Denver, CO?

For Junior Machine Learning jobs in Denver, CO, the most frequently searched job titles are:

What job categories do people searching Junior Machine Learning jobs in Denver, CO look for?

The top searched job categories for Junior Machine Learning jobs in Denver, CO are:

What cities near Denver, CO are hiring for Junior Machine Learning jobs?

Cities near Denver, CO with the most Junior Machine Learning job openings:

Junior Machine Learning jobs near you
Infographic showing various Junior Machine Learning job openings in Denver, CO as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 21% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $57,710 per year, or $27.7 per hour.

Machine Learning Engineer / MLOps Engineer

Bet365

Denver, CO • On-site

Full-time

Re-posted 28 days ago

Bet365 rating

8.9

Company rating: 8.9 out of 10

Based on 14 frontline employees who took The Breakroom Quiz

2nd of 15 rated gambling companies

Job description

hackajob is collaborating with Bet365 to connect them with exceptional professionals for this role.

We are seeking a highly pragmatic, results-driven Machine Learning (ML) Engineer to join our newly established US Data team. In this role, you will build the reliable, automated infrastructure that powers our machine learning lifecycle.

Your primary mission is to operationalize and scale the models developed by our data science team, taking them from prototype to robust, production-grade systems with high velocity.

You’ll focus on building reliable, automated and maintainable systems, keeping solutions pragmatic rather than over-engineered. You will also be passionate about automation, software engineering excellence, and MLOps.

You will report to the Data Science Team Leader and work in close alignment with the US AgentOps Team Lead (responsible for agentic and model orchestration platforms) and our UK technical excellence center. You will act as the bridge between model development and reliable platform engineering.

Preferred Skills and Experience

  • Proven experience as an ML Engineer, Data Engineer, or Software Engineer with a clear focus on deploying, monitoring, and scaling machine learning systems in production.

  • A pragmatic, proactive approach to system design, prioritizing speed, reliability, and business value over complex, theoretical infrastructure.

  • Strong Python programming skills, with a solid grasp of software engineering patterns, API development, and automated testing frameworks.

  • Extensive hands-on experience with Google Cloud Platform (GCP).

  • Practical experience with Vertex AI (specifically Vertex AI Pipelines, Endpoints, and Workbench).

  • Proficiency with containerization (Docker) and container orchestration tools.

  • Excellent communication skills, with the ability to translate software engineering concepts for data scientists and operational requirements for product leads.

  • Experience utilizing Infrastructure as Code (IaC) tools such as Terraform.

  • Experience running containerized workloads on Google Kubernetes Engine (GKE).

  • Familiarity with real-time streaming tools like Apache Kafka or GCP Pub/Sub.

What you will be doing

  • Owning the deployment of machine learning models to production. Build and maintain scalable, low-latency prediction endpoints using GCP Vertex AI.

  • Designing, implementing, and maintaining CI/CD/CT (Continuous Integration, Continuous Delivery, Continuous Training) pipelines for machine learning workflows using Vertex AI Pipelines, Cloud Build, and related GCP tools.

  • Setting up automated monitoring and alerting frameworks (e.g., Vertex AI Model Monitoring) to track data drift, model drift, and system performance in real-time.

  • Championing best practices for software engineering within the Data Science team, including robust unit testing, containerization, version control, and CI/CD automation.

  • Working closely with the Data Science Team Leader, Junior Data Scientists, and the AgentOps Team Lead to accelerate deployment cycles, remove operational bottlenecks, and maintain high deployment velocity.

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