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Amazon Sagemaker Jobs in Colorado (NOW HIRING)

Vertafore

Sr. Data Scientist

Denver, CO · On-site

... Amazon SageMaker/AWS machine learning tasks: • classification • regression • clustering • dimensionality reduction • natural language processing • recommender systems machine learning ...

New

The Mutual Group

Lead AI Engineer - AWS Platform

Denver, CO · On-site +1

$130K - $190K/yr

Amazon SageMaker, AWS Lambda, S3, Glue, EKS, and related services * Contribute to evolving use of AWS Bedrock Apply Responsible AI Practices * Implement guardrails for LLM-based systems (grounding ...

1872 Consulting

Senior Data Scientist

Boulder, CO · On-site

Exposure to Amazon's Glue, Athena, and SageMaker services will help you ramp up * Ability to design and code reports that showcase modeling's value * The instinctive thought process for determining ...

Aegon

Senior Appian Software Engineer

Denver, CO · On-site

$126K - $166K/yr

MS Cosmos DB, Apache Cassandra, Amazon DynamoDB) * Understanding of cloud services (e.g. AWS/Azure ... Knowledge of developing distributed computing (MS HPC, Sagemaker, Spark) * Two years of experience ...

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Amazon Sagemaker information

See Colorado salary details

$24.2K

$81.1K

$128.3K

How much do amazon sagemaker jobs pay per year?

As of Jul 26, 2026, the average yearly pay for amazon sagemaker in Colorado is $81,102.00, according to ZipRecruiter salary data. Most workers in this role earn between $61,000.00 and $100,900.00 per year, depending on experience, location, and employer.

What are Amazon SageMaker training jobs?

Amazon SageMaker training jobs are processes that train machine learning models using specified datasets and algorithms within the SageMaker environment. They involve configuring training parameters, selecting instance types, and monitoring progress through the SageMaker console or APIs. These jobs enable scalable, managed training for developing accurate models efficiently.

What is Amazon SageMaker?

Amazon SageMaker is a fully managed machine learning service provided by AWS that allows developers and data scientists to build, train, and deploy machine learning models quickly and at scale. It offers a range of tools for every stage of the ML workflow, including data labeling, model training, tuning, and deployment. SageMaker supports popular ML frameworks and integrates with other AWS services, making it easier to operationalize machine learning in the cloud. Its managed infrastructure helps reduce the time and complexity involved in developing ML solutions.

What are some common challenges faced by professionals working with Amazon SageMaker, and how can they be addressed?

Professionals working with Amazon SageMaker often encounter challenges such as managing large datasets, optimizing model training costs, and integrating SageMaker with other AWS services or existing data pipelines. Addressing these challenges typically involves leveraging SageMaker's built-in data preprocessing features, using managed spot training to reduce costs, and collaborating closely with data engineering and DevOps teams to ensure seamless integration. Regularly reviewing AWS documentation and best practices can also help professionals stay updated on new features and solutions.

What is the purpose of Amazon SageMaker processing jobs?

Amazon SageMaker processing jobs are used by data scientists and machine learning engineers to perform data preprocessing, feature engineering, model evaluation, and inference tasks at scale. These jobs enable efficient data handling and model validation within the SageMaker environment, supporting the development and deployment of machine learning models.

Are AWS jobs still in demand?

Amazon SageMaker jobs and other AWS roles remain in demand due to the growing adoption of cloud computing and machine learning. Skills in cloud services, data analysis, and AI tools are highly sought after, with many organizations expanding their cloud infrastructure and AI capabilities.

What are the key skills and qualifications needed to thrive as an Amazon SageMaker Machine Learning Engineer, and why are they important?

To excel as an Amazon SageMaker Machine Learning Engineer, you need strong expertise in machine learning concepts, data preprocessing, and programming languages such as Python, along with a degree in computer science or a related field. Familiarity with AWS SageMaker, cloud infrastructure, version control systems like Git, and relevant certifications such as AWS Certified Machine Learning – Specialty are highly beneficial. Exceptional problem-solving, communication, and collaboration skills help you work effectively with cross-functional teams and stakeholders. These skills are vital for building, deploying, and maintaining scalable machine learning solutions that drive business value.

What can I do with Amazon SageMaker?

Amazon SageMaker is a cloud-based machine learning platform that allows data scientists and developers to build, train, and deploy machine learning models at scale. It provides tools for data labeling, model tuning, and deployment, enabling efficient development of AI solutions. Users can also utilize built-in algorithms and integrate with other AWS services for comprehensive machine learning workflows.
What are popular job titles related to Amazon Sagemaker jobs in Colorado? For Amazon Sagemaker jobs in Colorado, the most frequently searched job titles are:
What job categories do people searching Amazon Sagemaker jobs in Colorado look for? The top searched job categories for Amazon Sagemaker jobs in Colorado are:
Amazon Sagemaker jobs near you
Infographic showing various Amazon Sagemaker job openings in Colorado as of July 2026, with employment types broken down into 58% Full Time, 6% Temporary, and 36% Contract. Highlights an 74% In-person, and 26% Remote job distribution, with an average salary of $81,102 per year, or $39 per hour.
Software Development Engineer, Measurement, Ad Tech, and Data Science (MADS)

Software Development Engineer, Measurement, Ad Tech, and Data Science (MADS)

Amazon

Boulder, CO

$118K - $142K/yr

Full-time

Posted 3 days ago

Amazon rating

7.4

Company rating: 7.4 out of 10

Based on 7,012 frontline employees who took The Breakroom Quiz

6th of 39 rated national retailers

Job description

Application deadline: Jul 31, 2026
Turn billions of advertising signals into real-world outcomes - building the distributed systems that power ML model execution, audience activation, and campaign optimization for the world's largest brands on Amazon Marketing Cloud (AMC). You'll own services that process millions of audience memberships daily and orchestrate customer ML workloads with strict reliability, privacy, and latency requirements.
The AMC Custom Models and Signals team owns two high-impact domains: our Custom Models platform (ML orchestration via AWS Clean Rooms, SageMaker, and Spark) and our audience and custom signals publication pipeline (high-throughput identity resolution and segment delivery to Amazon DSP and Sponsored Ads). You'll work directly with large enterprise advertisers and see your work drive measurable advertising outcomes

We're looking for an engineer who writes production-quality code, cares about operational excellence, leverages AI efficiently, and wants to solve hard distributed systems problems at scale.
Key job responsibilities
- Design, build, and operate distributed services (Java, Lambda, Step Functions, DynamoDB, Spark) that orchestrate ML model training, inference, and audience activation
- Own end-to-end delivery of features across the Custom Models platform and audience/signals publishing pipeline - from API design through deployment and production monitoring
- Investigate and resolve complex production issues spanning multiple services and AWS dependencies (Step Functions, Clean Rooms, SageMaker, Glue, S3)
- Improve system reliability, observability, and operational tooling for multiple Ads Tier 1 services
- Collaborate with science, product, and partner teams to translate customer requirements into scalable technical solutions
- Participate in on-call rotation and drive operational excellence through COEs, runbooks, and automated monitoring
About the team
AMC Custom Models and Signals is a Boulder-based team building the systems that turn AMC analytics into real-world advertising outcomes. We own Custom Models (ML orchestration), Igno (audience publishing), AMS (identity resolution and delivery), and Custom Signals API (new activation signals). Our work directly powers audience targeting, measurement, and ML-driven optimization for enterprise advertisers

We value ownership, operational rigor, and solving hard problems simply - and we're investing heavily in GenAI and agentic approaches to platform reliability and customer experience.

What Amazon employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Amazon

Sourced by ZipRecruiter

Amazon.com, Inc., commonly known as Amazon, is an American multinational technology company. It was founded by Jeff Bezos in 1994 and initially started as an online marketplace for books. Since then, Amazon has expanded its operations and become one of the largest e-commerce companies in the world. Amazon's primary business is its online retail platform, where customers can purchase a vast array of products, including electronics, clothing, books, home goods, and much more. The company offers a convenient and user-friendly shopping experience, with features such as fast shipping, customer reviews, and personalized recommendations. In addition to its e-commerce platform, Amazon has diversified its business into various other areas. One of its notable ventures is Amazon Web Services (AWS), a comprehensive cloud computing platform that provides services such as storage, compute power, and database management to individuals and businesses. AWS has become a leader in the cloud computing industry, powering many websites and applications worldwide. Amazon has also developed its own consumer electronics, including the popular Amazon Kindle e-reader, Fire tablets, Fire TV streaming devices, and the Alexa-powered Echo smart speakers. The Alexa voice assistant, integrated into these devices, allows users to interact with their devices using voice commands, perform tasks, and access information. Furthermore, Amazon has expanded into media and entertainment. It operates Prime Video, a streaming service that offers a wide range of movies, TV shows, and original content. Amazon Music provides a platform for streaming and purchasing digital music, while Audible offers audiobooks and other audio content. The company's commitment to customer satisfaction and convenience is demonstrated by its membership program, Amazon Prime. Prime members receive various benefits, including free two-day shipping, access to streaming services, exclusive deals, and more.

Industry

It services, book publishers, retail, real estate and computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Seattle, WA, US

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