Data-Centric ML Engineer for Foundation Models

Apple Inc.; Cupertino, Northern (CA, KY); Hybrid; USD 150,000 - 278,000; Full time

Benefits offered by this job

Medical and dental coverage

Employee stock programs

Stock purchase plan

Relocation assistance

Job summary

Apple Inc. is seeking a senior data scientist for its Foundation Models team in Cupertino to shape data strategies, pipelines, and methodologies across the full training lifecycle.

You will collaborate with researchers, engineers, and product teams to identify capability gaps and create high‑quality training signals for reasoning, planning, and multimodal understanding. The role focuses on data-centric AI, synthetic data generation, model self‑evolution, and scalable data interventions to

Qualifications

• LLM and/or multi‑modal LLM expertise with a publications track record.
• Proficient in Python and DL toolkits such as JAX, PyTorch, or TensorFlow.
• Ph.D. or equivalent practical experience in a related technical field.

Responsibilities

• Drive data strategy across the foundation model training lifecycle (pre-, mid-, post-training).
• Design scalable data generation, curation, and quality assessment systems for text and multimodal data.
• Develop synthetic data pipelines enabling reasoning, planning, coding, tool use, and multimodal understanding.
• Create self‑improvement frameworks for foundation models to improve their own data and behaviors.
• Develop data flywheels turning model feedback and evaluations into high‑quality training signals.
• Advance benchmark‑driven methods to identify gaps and translate insights into interventions.
• Advance capabilities in reasoning, agentic systems, and long‑horizon tasks.
• Improve data‑centric AI techniques including reward modeling and alignment for foundation models.

Skills

LLM / Multi-modal LLM

Python

JAX

PyTorch

TensorFlow

Education

Ph.D. in Computer Science, ML, AI, or related field

Job description

Apple Inc. is seeking a senior data scientist for its Foundation Models team in Cupertino to shape data strategies, pipelines, and methodologies across the full training lifecycle.

You will collaborate with researchers, engineers, and product teams to identify capability gaps and create high‑quality training signals for reasoning, planning, and multimodal understanding. The role focuses on data-centric AI, synthetic data generation, model self‑evolution, and scalable data interventions to