APPLYDJINN

Machine Learning jobs and hiring demand in Cupertino, CA

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

Explore Machine Learning by location
Cupertino, CA

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

This reference guide is available while the observed market sample grows. Counts describe listings in our catalog; employer breadth and source-date coverage are still limited.

52core + required roles31context + optional mentions0remote-friendly▼ -81 this week

Employer concentration

Apple accounts for 80 of 83 mentions (96%). The remaining 3 mentions span other employers.

View without top 1 employer →
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.

83 live roles2 hiring companies0% 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.

34 Core18 Required11 Supporting20 Optional
52core + required63% of mentions
41.5roles per company2 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

Cupertino, CA80California, United States3Cupertino, California, United States3United States3

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 83 active roles; each role is counted once. Read the methodology.

Posting evidence, not a company-wide stack claim
Main / core
41%(34)
Required
22%(18)
Supporting / context
13%(11)
Nice-to-have
24%(20)

Skills mentioned with Machine Learning

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

AI62 together15 both required4 alternativesPython42 together24 both required1 alternativesScalability34 together7 both requiredC++26 together12 both requiredCloud26 together8 both required1 alternativesAlgorithms25 together7 both required

Role graph

Roles hiring for Machine Learning

Software Engineering 26Machine Learning & AI 22Data Science 5Project & Program Management 4Test Automation 4DevOps & Platform 3Algorithm & Scientific Engineering 2Data Engineering 2

Latest roles mentioning Machine Learning

Staff/Sr. ML Infrastructure Engineer, Foundation Model Compute Infra

Core

Apple — Cupertino, CA

Business Operations Engineering Program Manager - PACE

Nice

Apple — Cupertino, CA

Apple Services Engineering (ASE) Compute - Software Engineering Manager

Nice

Apple — Cupertino, CA

Senior Software Engineer, AI Tools - Special Projects

Context

Apple — Cupertino, CA

Senior Machine Learning Engineer, Developer Product Analytics

Core

Apple — Cupertino, CA

Mgr III, ASIC - AMZ25936.5

Context

Amazon — Cupertino, California, United States

View all 83 roles mentioning Machine Learning →

Original hiring measurements follow the ApplyDjinn data license and attribution rules. Employer descriptions, logos and trademarks remain with their owners.