Applied Research Software Engineer.

You are a technical leader with a demonstrated track record of identifying the right problems, taking calculated risks, and shipping at scale. The candidate will be a core contributor on an interdisciplinary team of specialized researchers and engineers focused on emerging technologies. As an Applied Research Software Engineer on this team, you will be responsible for leading end-to-end incubations from early ideation to deployment, applying state-of-the-art ML research, algorithmic innovation, and agentic systems to challenges with both immediate and long-term impact. You bring deep intellectual curiosity, the ability to operate at the frontier of new technologies, and excellent communication skills to align company leaders and cross-functional collaborators alike.

Description

The work is hands-on, highly cross-disciplinary, and central to shaping how Apple’s intelligent systems evolve. You’ll be working on the pipeline between research and product that unlocks the kinds of the delightful user experiences Apple is known for, iteratively crafting ideas that turn science fiction into reality. You care deeply about the process of applied scientific research, enjoy supporting technology investigations, and are comfortable jumping across multiple layers of the productization process when the team calls on you, from prototype through shipping. This is not a typical ML engineering role where you will be responsible for an existing system in a large team: instead, the work is equal parts interdisciplinary pathfinding and helping realize your projects as critical features of Apple’s next-gen products.

Minimum Qualifications

Bachelor’s, Master’s, or PhD in Computer Science or adjacent technical field
6+ years of engineering experience building and maintaining data-intensive systems - including feature pipelines, training infrastructure, or evaluation frameworks.
Strong proficiency in both Python and C++ with experience in complex, multi-language systems.
Expertise in deep learning frameworks such as PyTorch.
Familiarity with SOTA ML approaches (VLMs, diffusion models, RL, etc.)

Preferred Qualifications

Close to the frontier. You’re the person your friends come to when a new model drops or a new research paper emerges as state-of-the-art.
Experience with a specialty discipline such as robotics & machine perception, computer vision, graphics, or simulation.
An interest in contextual, personalized computing and the supporting principles of cognitive science and psychology.
A strong point of view on where human-AI interaction is headed — and a drive to be a leading part of creating ethical, privacy-centric products.
Experience with real-time or near-real-time systems and deeply integrated architectures that span local SoCs through cloud compute.
Familiarity with hardware bring-up, data communication protocols, or sensor integration.