Machine Learning jobs and hiring demand in Colorado, United States

Name: Machine Learning hiring demand
Creator: ApplyDjinn

Core and required roles are separated from contextual and optional mentions so the headline does not overstate job intent.

Explore Machine Learning by location

Colorado, United States

Select up to five markets; results and hiring evidence update together.

5core + required roles13context + optional mentions2remote-friendly▼ -2 this week

Employer concentration

Amazon accounts for 11 of 18 mentions (61%). The remaining 7 mentions span other employers.

View without top 1 employer →View without top 3 employers →

Machine Learning market intelligence visualization

Model work connected to product and operational evidence

Machine Learning market guide

Machine-learning demand covers model development, applied engineering, data pipelines, evaluation, platform work, and domain-specific products. The job title alone rarely reveals which part of the lifecycle matters most.

18 live roles5 hiring companies11% remote-friendly

Vacancy evidence map

Where Machine Learning carries hiring weight

Each segment represents roles where Machine Learning was parsed at that evidence tier. This keeps a required production skill separate from contextual stack language.

2 Core3 Required11 Supporting2 Optional

5core + required28% of mentions

3.6roles per company5 employers

Market interpretation

What Machine Learning demand means in practice

ApplyDjinn’s evidence tiers help distinguish roles where ML is the product or primary craft from roles where it is supporting context. Companion technologies show whether employers emphasize experimentation, data engineering, deployment, inference, computer vision, NLP, or generative AI.

Strong applications explain the data, baseline, metric, validation approach, deployment boundary, and monitoring plan. Model choice matters, but evidence of reliable evaluation and product impact usually travels farther than an isolated accuracy claim.

Applied MLML platform / MLOpsData scienceAI product engineering

Reading the market

Signals to look for

Python, SQL, cloud, data pipelines, and model frameworks form the recurring foundation.
MLOps and platform roles emphasize reproducibility, deployment, observability, and cost.
Applied roles often value domain data and product judgment alongside modelling depth.

Geographic demand

Top Machine Learning locations

Colorado, United States18 United States18 Denver, Colorado, United States14 California, United States3 District of Columbia, United States3 Texas, United States3 Washington, District of Columbia, United States3 Arizona, United States2

Questions candidates ask

Machine Learning hiring FAQ

What is the difference between AI and machine-learning vacancies?

AI is a broader market label that can include workflow and product roles; ML vacancies more often require model, data, evaluation, or deployment depth.

What portfolio evidence is useful for ML roles?

A reproducible project with a baseline, honest validation, error analysis, deployment considerations, and a clear account of personal contribution.

How Machine Learning appears in job requirements

Inferred from the title and requirement wording in 18 active roles; each role is counted once. Read the methodology.

Posting evidence, not a company-wide stack claim

Main / core: 11%(2)
Required: 17%(3)
Supporting / context: 61%(11)
Nice-to-have: 11%(2)

Skills mentioned with Machine Learning

A shared posting does not always require both skills. Explicit alternatives are counted separately.

AI13 together3 both required AWS10 together0 both required Cloud10 together0 both required Scalability9 together0 both required TypeScript8 together0 both required GenAI7 together0 both required

Role graph

Roles hiring for Machine Learning

Software Engineering 8 Business Operations 2 Supply Chain & Logistics 2 Customer Success 1 Data Science 1 Machine Learning & AI 1 Operations 1 Sales & Account Management 1