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Machine Learning Engineer Python Jobs in Colorado

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Senior Machine Learning Engineer I // II

Denver, CO · On-site +1

$107K - $147K/yr

The Senior Machine Learning Engineer will join our ML team. This team is responsible for building ... Proficiency in Python , SQL, key ML libraries, and Spark * Mindset: A strong outcome-oriented ...

Quora

$139K - $168K/yr

Our team of Machine Learning Engineers have high impact by advancing the current Machine Learning ... Strong knowledge of Python or C++, or the ability to learn them quickly * A passion for learning ...

Quora

$139K - $168K/yr

Our team of Machine Learning Engineers have high impact by advancing the current Machine Learning ... Strong knowledge of Python or C++, or the ability to learn them quickly * A passion for learning ...

Quora

$139K - $168K/yr

Our team of Machine Learning Engineers have high impact by advancing the current Machine Learning ... Strong knowledge of Python or C++, or the ability to learn them quickly * A passion for learning ...

Quora

$139K - $168K/yr

Our team of Machine Learning Engineers have high impact by advancing the current Machine Learning ... Strong knowledge of Python or C++, or the ability to learn them quickly * A passion for learning ...

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Machine Learning Engineer Python information

What is a machine learning engineer python?

A Machine Learning Engineer Python is a professional who uses the Python programming language to design, build, and deploy machine learning models and systems. They work with large datasets, develop algorithms, and use Python libraries such as TensorFlow, scikit-learn, and PyTorch to solve complex problems. Their responsibilities also include preprocessing data, training models, evaluating performance, and integrating solutions into production environments. Machine Learning Engineers often collaborate with data scientists, software engineers, and business stakeholders to create scalable and efficient machine learning applications.

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

To thrive as a Machine Learning Engineer Python, you need a solid background in computer science, statistics, and mathematics, along with proficiency in Python programming and machine learning concepts. Familiarity with frameworks such as TensorFlow, PyTorch, Scikit-learn, and experience with cloud platforms or MLOps tools are highly valued, as are certifications like Google Professional Machine Learning Engineer. Strong problem-solving abilities, communication skills, and a collaborative mindset help set you apart in this field. These skills enable engineers to design, implement, and deploy effective machine learning solutions that address real-world challenges in dynamic, team-oriented environments.

What are some common challenges faced by machine learning engineers working with Python, and how can they be addressed?

Machine Learning Engineers using Python often encounter challenges such as managing large datasets, ensuring efficient model deployment, and maintaining reproducibility of experiments. Handling data pipelines and model versioning can be complex, especially as projects scale. To address these issues, engineers typically use tools like Pandas and Dask for data handling, Docker for containerization, and MLflow or DVC for tracking experiments and models. Collaborating closely with data engineers, software developers, and product teams is also essential to streamline workflows and ensure models are production-ready.

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

AspectMachine Learning Engineer PythonData Scientist
Required CredentialsBachelor's/Master's in CS, Data Science, or related; Python skills; ML certificationsBachelor's/Master's in Statistics, CS, or related; Python/R skills; Data analysis certifications
Work EnvironmentDevelops scalable ML models, deploys algorithms, collaborates with engineering teamsAnalyzes data, builds models, interprets results, communicates insights
Employer & Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research institutions

While both roles require Python proficiency and data skills, Machine Learning Engineers focus on building and deploying scalable ML models, whereas Data Scientists analyze data and generate insights. The roles often overlap but differ in their primary focus and responsibilities.

What are popular job titles related to Machine Learning Engineer Python jobs in Colorado?

For Machine Learning Engineer Python jobs in Colorado, the most frequently searched job titles are:

What job categories do people searching Machine Learning Engineer Python jobs in Colorado look for?

The top searched job categories for Machine Learning Engineer Python jobs in Colorado are:

What cities in Colorado are hiring for Machine Learning Engineer Python jobs?

Cities in Colorado with the most Machine Learning Engineer Python job openings:

Machine Learning Engineer Python jobs near you
Infographic showing various Machine Learning Engineer Python job openings in Colorado as of July 2026, with employment types broken down into 91% Full Time, 6% Part Time, and 3% Contract. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution.

Machine Learning Engineer / MLOps Engineer

Bet365

Denver, CO

Full-time

Re-posted 14 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.

What Bet365 employees say

Pay

Benefits

Hours and flexibility

Workplace

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