Applications of ML Engineering Manager

Apple Apple · Big Tech · San Francisco, CA · Machine Learning and AI

Manager for Responsible Development & Safety in Apple Services Engineering, focusing on shaping policies, evaluating AI models and applications, and ensuring safe deployment of user-facing features. The role involves leading a team, collaborating with various cross-functional teams, and developing evaluation processes for AI/ML models.

What you'd actually do

  1. Shape and support implementation of safety policies defined by AI Product teams.
  2. Collaborate with AIML, Product & Design, Privacy, Human-centered AI, policy leads, and business owners to define model and application evaluation requirements, set priorities, and scope and manage execution of AI /ML model and application evaluations.
  3. Work with Senior Managers to set objectives and key results and the long-term strategy for safety evaluation, including benchmarks, automated methods, and ongoing monitoring of safety performance of AI models.
  4. Lead a team of individual contributors.
  5. Develop new programming and generate opportunities for ASE to regularly engage with Policy, Legal, and Privacy teams.

Skills

Required

  • Ph.D., J.D. or equivalent experience in Computer Science, Machine Learning, and/or Human-Computer Interaction
  • 5+ years proven experience with AI product and/or user policies in multiple regions of the world
  • Python for data analysis (pandas, NumPy, Jupyter, etc.)
  • Ability to design taxonomies, categorization schemes, or structured rating frameworks.

Nice to have

  • A strong understanding of the challenges and issues in the fields of Responsible AI and/or Safety, through AI and society experience (e.g. relevant governance, policy, legal or research).
  • Experience working directly with LLMs, LMMs, and generative AI and agentic systems, in a development and/or evaluation capacity.
  • Experience leading new teams working on innovative initiatives within a well-established organization.
  • 5+ years experiences with AI evaluations, managed with a diverse sets of collaborators, such as Product, Engineering, Security, and Legal teams.
  • Experience navigating and assessing complex societal questions related to technology development, including balancing the benefits and risks of research and applications.
  • Strong management skills, including the ability to communicate effectively in tight turnaround times.
  • Excellent experience collaborating with technical stakeholders and highly interdisciplinary teams.
  • Strong communication skills, both written and verbal.
  • Experience working with large datasets, annotation tools, or model-evaluation pipelines.
  • Strong ability to stitch together qualitative and quantitative findings into actionable guidance.

What the JD emphasized

  • ensure safe and responsible deployment
  • safety best practices
  • AI model performance
  • safety development ecosystem
  • safety policies
  • model and application evaluation requirements
  • AI /ML model and application evaluations
  • safety evaluation
  • safety performance of AI models
  • Responsible AI
  • Safety
  • AI evaluations

Other signals

  • Responsible AI
  • Safety policies
  • Model and application evaluation
  • Risk mitigation
  • LLMs, LMMs, and generative AI and agentic systems