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Apple Senior Applied ML Scientist Generative AI United States, Washington, Seattle 241317992
Yesterday
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In this role, you will help us develop an innovative, AI-driven evaluation ecosystem in order to accelerate and empower AI development at Apple. Working at the intersection of applied research, ML & GenAI engineering, and tool development, you will champion principles of iterative experimentation, innovation, and enablement.Your work will span the full development lifecycle—from prototyping new ideas to designing and deploying reliable, production grade systems. You'll solve fundamental problems in AI evaluation, such as developing innovative LLM-judges, automating error analysis, methods for validation data, and optimizing human-AI collaboration, all while pushing the boundaries of core AI capabilities.
- Strong foundation in machine learning fundamentals with the ability to tackle sophisticated ML challenges.
- Experience or proven curiosity about designing and implementing AI-driven approaches to evaluation (e.g. LLM-as-a-judge, automated evaluation, etc).
- Demonstrated ability to develop high-impact language model systems for real-world applications.
- Expertise in GenAI, LLM, and/or NLP/NLU evaluation.
- You have the demonstrated ability to identify research directions, rapidly prototype solutions, and drive them to practical impact.
- Proficient in software engineering standard methodologies (e.g., modular software design, testing).
- Strong proficiency in Python.
- Strong proficiency PyTorch, TensorFlow, or Jax.
- Excellent communication skills with a proven ability to engage diverse collaborators.
- Experience with MLOps standards, including containerization, orchestration (e.g., Kubernetes), and CI/CD.
- 5+ years with a Master's degree, 3+ years with a PhD, or equivalent practical experience.
- You bring validated experience developing and owning high-impact, developer-facing systems and tools.
- You have experience evaluating sophisticated agentic systems using LLMs.
- Experience adapting and aligning LLMs through various training strategies, e.g. continued pre-training, supervised fine-tuning, and reinforcement learning.
- Expertise in uncertainty estimation and calibration, active learning, or related problem spaces.
- Experience with ML platform design or ownership
- Hands-on experience with large-scale data processing frameworks, e.g. Spark, PySpark, Dask, or Ray.
- Track record of contributions to open-source ML projects or publications in top-tier ML conferences (e.g., NeurIPS, ICML, ACL).
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
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