CV/ML Generalist Engineer

Apple is looking for a creative, versatile Computer Vision and Machine Learning (CVML) Generalist Engineer to join a team of leading researchers and engineers. The team builds generative machine learning-based systems and frameworks for image and video generation, with a focus on human subjects. You'll work in a fast-paced, startup-like environment and help the team scale through smart engineering and AI-driven tools.

You'll also work with cross-functional partners to build the algorithms and systems behind Apple's next-generation products.

The ideal candidate has both strong computer vision and ML foundations and hands-on engineering experience with agentic workflows. You're comfortable owning complex systems from start to finish: building, evaluating, and shipping them. You're self-driven, act without waiting for direction, and communicate well with people at every level of the organization.

You'll be a strong candidate if you have deep experience in two or more of the following areas:

• Generative CV/ML research and algorithm development, especially image and video synthesis
• Building large-scale, end-to-end computer vision systems with AI coding agents
• Human-centric computer vision problems, such as digital humans, motion synthesis, body tracking, or generative avatars
• Neural rendering or world models

BS in Computer Science or a related field and 3+ years of relevant experience, or equivalent experience.

High agency: you take ownership and drive work forward on your own.

Excellent problem-solving, critical-thinking, and communication skills.

Strong theoretical and practical knowledge of machine learning and deep learning.

Experience building and evaluating LLM/VLM-powered agents, including tool use, multi-step reasoning, planning, and context management.

Strong programming skills in Python and/or C++, and experience with a deep learning framework such as PyTorch or JAX.

PhD in Computer Science, Machine Learning, or a related field.

Deep expertise in computer vision and machine learning, with a strong focus on deep generative models (e.g., GANs, VAEs, diffusion models)

Knowledge of modern vision data visualization methods, such as 3D Gaussian Splatting, neural volume rendering, is a plus

Familiarity with modern graphics technology for high-fidelity 3D human generation, such as photogrammetry, computational hair modeling, physics-based hair/cloth simulation, or physically-based shading, would be a plus

Track record of published research in top-tier computer vision conferences such as CVPR, ICCV, ECCV, NeurIPS, or other leading forums, demonstrating your contributions to advancing the state-of-the-art in computer vision and machine learning, is a plus