Algorithm Evaluation Manager

AppleSunnyvale, CA

About The Position

We are looking for an experienced and highly motivated Engineering Manager to lead a dynamic team focused on machine learning algorithms or Large Language Model (LM) evaluation. In this role, you will guide a team responsible for the critical data and evaluation pipelines that ensure our models are accurate, robust, and performant. The ideal candidate will bring a strong mix of technical leadership, expertise in data curation and annotation processes, and deep analytical skills. You will collaborate closely with cross-functional research and engineering teams, requiring exceptional communication and strategic thinking. As the Engineering Manager for this team, you will be at the forefront of our AI/ML development lifecycle.

Requirements

  • BS and a minimum of 10 years relevant industry experience
  • 2+ years of direct people management experience, with a track record of hiring, mentoring, and leading high-performing technical teams.
  • Proven experience in model evaluation, benchmarking, and A/B testing methodologies for machine learning models (Computer Vision or Foundation Models).
  • Familiarity with the design and architecture of machine learning inference pipelines and underlying infrastructure.
  • Hands-on experience designing and managing data curation strategies and human-in-the-loop annotation processes.
  • Strong analytical skills with the ability to dive deep into datasets to identify trends, biases, and areas for model improvement.
  • Excellent verbal and written communication skills, with the ability to translate complex technical concepts to both technical and non-technical stakeholders.

Nice To Haves

  • PhD in Computer Science, Machine Learning, or a related field.
  • Expertise in both Computer Vision (CV) algorithms and Large Language Model (LM) evaluation methodologies (e.g., RLHF, prompt evaluation).
  • Experience scaling large data operations, managing complex annotation workflows, and working directly with external data vendors.
  • Familiarity with Python, SQL, and ML frameworks (e.g., PyTorch) to effectively review technical work and guide engineering decisions.
  • Demonstrated ability to drive strategic alignment across downstream product teams, ML researchers, and platform engineers in a highly matrixed environment.

Responsibilities

  • Team Leadership: Lead, mentor, and grow a team of engineers and data specialists. Foster a culture of innovation, rigorous analysis, and continuous learning.
  • Evaluation Strategy: Define and execute the evaluation strategy for both CV and LM models. Build robust, scalable evaluation pipelines that accurately reflect real-world performance.
  • Data Pipeline Management: Oversee the end-to-end data lifecycle. This includes establishing data curation guidelines, managing data quality, and optimizing large-scale annotation workflows with external vendors or internal teams.
  • Analytical Deep Dives: Guide the team in performing rigorous data analysis to troubleshoot model regressions, uncover data quality issues, and identify opportunities for algorithmic improvements.
  • Strategic Alignment: Act as the primary point of contact for your team, communicating progress, bottlenecks, and strategic data needs to leadership and partner teams.
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