Machine Learning Engineer

Apple Inc

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Cupertino, CA
Salary
$150,400–$277,600 / yr
Posted
12 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $219k
This role $214k
$135k most similar roles pay here $293k

This role pays more than 55% of similar roles. Most pay $191,125–$246,150 — the shaded band above. At the midpoint, this role pays about $214k versus about $219k for comparable roles.

Based on 240 similar postings.

Employer

About Apple Inc

Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software

Apple Inc currently has 2925 open roles on FindRole.

Listed pay typically runs $166,600–$277,600 across 2305 roles with salary data.

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At a glance

TL;DR · Machine Learning Engineer

The Machine Learning Engineer joins the team to apply advanced mathematical modeling, statistical analysis, and optimization algorithms to solve complex manufacturing challenges. This role focuses on production optimization by developing models for capacity, yield, cycle times, and supply chain metrics. The engineer will build simulation models to create digital twins of assembly lines to identify bottlenecks and perform what-if scenarios while implementing data fusion techniques to generate manufacturing intelligence. Required technical skills include proficiency with solvers like Gurobi, CPLEX, CP-SAT, Pyomo, and GAMS; experience with simulation platforms such as Arena, FlexSim, SimPy, and AnyLogic; and machine learning tools including PyTorch and Scikit-learn. Preferred expertise includes GenAI agentic workflows, LLM fine-tuning, and infrastructure tools like Kubernetes, Apache Airflow, Docker, and Ray to manage large-scale training and inference.

What does a Machine Learning Engineer earn in California?

Median $238250 from 175 postings across 25 companies.

See salary data

What you'll do

  • Develop and implement mathematical models to optimize factory capacity, yield, cycle times, and supply chain metrics.
  • Build and maintain simulation models to create "digital twins" of assembly lines for bottleneck identification.
  • Test "what-if" capacity scenarios using advanced simulation modeling techniques.
  • Implement data fusion techniques to integrate diverse operational data sources into actionable manufacturing intelligence.
  • Apply optimization algorithms and statistical analysis to solve complex manufacturing and supply chain challenges.
  • Translate complex mathematical theories into simple, universal language for non-technical stakeholders.
  • Develop GenAI applications featuring agentic workflows and fine-tuned LLM/LMM models.
  • Utilize distributed computing and orchestration tools like Kubernetes and Airflow for large-scale model training.

What we're looking for

  • Master's degree or PhD in Operations Research, Industrial Engineering, Management Science, Applied Mathematics, or a related field.
  • Proficiency with solvers and modeling languages such as Gurobi, CPLEX, CP-SAT, Pyomo, and GAMS.
  • Hands-on experience with simulation platforms like Arena, FlexSim, SimPy, and AnyLogic.
  • Experience with machine learning platforms such as PyTorch and Scikit-learn.
  • Excellent communication skills to explain complex mathematical theories to non-technical stakeholders.
  • Proven experience in GenAI application building with agents and agentic workflows (preferred).
  • Experience with LLM and LMM development and fine-tuning (preferred).
  • Proficiency in using cutting-edge GenAI tools like Claude Code or Roo Code (preferred).

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