Sr Machine Learning Engineer, Tech Lead — Autograder Systems, Evaluation

Apple Inc

Quick summary

Work type
On-site
Location
Cupertino, CA
Salary
$181,100–$318,400 / yr
Posted
41 days ago

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $224k
This role $250k
$165k most similar roles pay here $335k

This role pays more than 74% of similar roles. Most pay $197,962–$250,756 — the shaded band above. At the midpoint, this role pays about $250k versus about $224k for comparable roles.

Based on 239 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 638 open roles on FindRole.

Listed pay typically runs $171,600–$272,100 across 505 roles with salary data.

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At a glance

TL;DR · Sr Machine Learning Engineer, Tech Lead — Autograder Systems, Evaluation

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As a Senior Machine Learning Engineer Tech Lead at Apple’s centralized evaluation organization, you will lead the technical vision for evaluating 20+ generative AI features by pioneering state-of-the-art methods and building a robust autograder training pipeline. Your day-to-day responsibilities include researching novel techniques like reward modeling and LLM-as-judge, architecting scalable systems for data curation and model fine-tuning, and designing iterative improvement loops to enhance autograder performance. You will also establish quality benchmarks and collaborate with teams across the organization to ensure high-quality AI experiences used by millions of people. The role requires deep expertise in prompt-tuning techniques, proficiency in Python and PyTorch, and a strong background in ML systems engineering, data quality, and human-in-the-loop annotation pipelines.

Skills

Python PyTorch LLM VLM SFT RLHF DPO Model Calibration Uncertainty Estimation Data Flywheel Design CI/CD

What you'll do

  • Define and drive the technical roadmap for autograder quality using novel methods like reward modeling and LLM-as-judge.
  • Architect a scalable autograder training pipeline including data curation, model fine-tuning, and versioning.
  • Design and own an iterative hillclimbing system to improve autograder performance through prompt and model optimization.
  • Establish benchmarks and metrics for tracking the quality and reliability of autograder outputs.
  • Mentor and guide a team of MLEs in design reviews and modeling standards, fostering continuous learning.

What we're looking for

  • Master's or PhD in Computer Science, Machine Learning, AI, or related field.
  • 5+ years of industry experience in machine learning with focus on LLM/VLM systems.
  • Deep expertise in prompt-tuning and fine-tuning techniques, model calibration, uncertainty estimation.
  • Familiarity with data flywheel design for continuous improvement of training data.
  • Proficiency in Python and ML frameworks like PyTorch.

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