Loading...
Sr Machine Learning Engineer, Tech Lead — Autograder Systems, Evaluation
Apple
H1B ✓On-sitetechnical leadPosted April 24, 2026
About the Role
You will define the technical roadmap for autograder quality and lead a team of engineers to build scalable training and hillclimbing systems. This role involves collaborating with cross-functional partners to ensure model outputs are accurate and continuously improved across generative AI features.
Requirements
Candidates must hold a Master's or PhD in Computer Science, Machine Learning, or a related field with at least 5 years of industry experience. Deep expertise in LLM/VLM systems, fine-tuning techniques, and ML systems engineering is required.
Full Job Description
We are looking for a Senior MLE Tech Lead to join a centralized evaluation organization and define the next generation of autograder quality across 20+ of Apple's most visible generative AI features. You will own the end-to-end technical vision for how we evaluate model outputs at scale — pioneering state-of-the-art methods, raising the technical bar, and leading a team of talented MLEs to build a robust autograder training and hillclimbing system from the ground up.
This is a high-impact, hands-on leadership role at the intersection of model evaluation, data quality, and ML systems engineering. You will work closely with model developers, data teams, and product partners to ensure our autograders are fast, accurate, and continuously improving — directly shaping the quality of AI experiences used by hundreds of millions of people.
In this role you will focus on: Technical Leadership * Define and drive the technical roadmap for autograder quality — researching and introducing novel methods such as reward modeling, LLM-as-judge, preference learning, and calibration techniques to measurably improve evaluation accuracy. * Architect and lead the build-out of a scalable autograder training pipeline encompassing data curation, model fine-tuning, evaluation harnesses, and versioning. * Design and own the hillclimbing system that iteratively improves autograder performance through systematic prompt and model optimization loops. * Establish quality benchmarks, confidence metrics, and failure analysis frameworks that enable the team to track, trust, and act on autograder outputs. People & Collaboration * Mentor and technically guide a team of MLEs through design reviews, modeling standards, and hands-on problem-solving — fostering a culture of rigor and continuous learning. * Partner with data annotation teams to define labeling guidelines that feed autograder training. * Collaborate with feature engineers to align autograder signals with broader training and product objectives. * Translate complex technical trade-offs into clear narratives for engineering, product, and leadership audiences.
Master's or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field. 5+ years of industry experience in machine learning, with a strong focus on LLM or VLM systems. Deep expertise in prompt-tuning and fine-tuning techniques (SFT, RLHF, DPO, or equivalent), with proven experience of model calibration and uncertainty estimation. Familiarity with data flywheel design — leveraging model outputs to continuously improve future training data. Proficiency in Python and ML frameworks (PyTorch preferred).
Strong ML systems instincts — you care deeply about data quality, reproducibility, latency, and scale. Background in human-in-the-loop annotation pipelines and inter-annotator agreement analysis. Prior experience on an evaluation infrastructure or model quality team.
Description
In this role you will focus on: Technical Leadership * Define and drive the technical roadmap for autograder quality — researching and introducing novel methods such as reward modeling, LLM-as-judge, preference learning, and calibration techniques to measurably improve evaluation accuracy. * Architect and lead the build-out of a scalable autograder training pipeline encompassing data curation, model fine-tuning, evaluation harnesses, and versioning. * Design and own the hillclimbing system that iteratively improves autograder performance through systematic prompt and model optimization loops. * Establish quality benchmarks, confidence metrics, and failure analysis frameworks that enable the team to track, trust, and act on autograder outputs. People & Collaboration * Mentor and technically guide a team of MLEs through design reviews, modeling standards, and hands-on problem-solving — fostering a culture of rigor and continuous learning. * Partner with data annotation teams to define labeling guidelines that feed autograder training. * Collaborate with feature engineers to align autograder signals with broader training and product objectives. * Translate complex technical trade-offs into clear narratives for engineering, product, and leadership audiences.
Minimum Qualifications
Master's or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field. 5+ years of industry experience in machine learning, with a strong focus on LLM or VLM systems. Deep expertise in prompt-tuning and fine-tuning techniques (SFT, RLHF, DPO, or equivalent), with proven experience of model calibration and uncertainty estimation. Familiarity with data flywheel design — leveraging model outputs to continuously improve future training data. Proficiency in Python and ML frameworks (PyTorch preferred).
Preferred Qualifications
Strong ML systems instincts — you care deeply about data quality, reproducibility, latency, and scale. Background in human-in-the-loop annotation pipelines and inter-annotator agreement analysis. Prior experience on an evaluation infrastructure or model quality team.