Machine Learning Researcher Agentic Post-Training for On-Device Models

The Video Computer Vision (VCV) organization is an applied research and engineering team developing next-generation multimodal intelligence technologies across Apple products. Within VCV our team builds intelligent systems that combine multimodal understanding reasoning and tool use to deliver compelling user experiences. Our work is deeply integrated into the Apple ecosystem partnering across hardware software and ML teams to deliver real-time scalable and privacy-preserving experiences reaching millions of users.

In this role you will develop post-training methods that turn foundation models into capable reliable and efficient agents for on-device execution. You will develop supervised fine-tuning and reinforcement learning methods for agentic models alongside the training data interactive environments reward signals and evaluations needed to advance reasoning tool use and long-horizon tasks. A central research challenge is enabling smaller models to make effective decisions and complete multi-step tasks within tight latency memory and energy budgets. You will investigate how to improve reasoning planning and tool use while reducing reliance on lengthy generated reasoning and large inference budgets. Research directions may include structured reasoning adaptive computation and training objectives that balance task success with computational cost. You will own the projects end to end: identify capability gaps formulate hypotheses build training pipelines conduct rigorous experiments and translate frontier research into Apple products that deliver meaningful value to users.

Advance agentic post-training for on-device models. Develop supervised fine-tuning reinforcement learning preference optimization and distillation methods that improve reasoning and multi-step tool use in on-device training data and interactive environments. Build scalable pipelines for trajectory generation curriculum construction and hard-example mining. Develop environments that support learning through tool use and multimodal reward functions verifiers and graders for complex agent tasks addressing long-horizon credit assignment supervision quality and gaps between reward scores and actual task capability under deployment constraints. Research methods that balance task success with latency memory energy and tool-use costs including structured or compressed reasoning and adaptive rigorous evaluations and diagnostics. Measure task success generalization reliability and efficiency. Analyze model outputs and agent trajectories to distinguish modeling limitations from data reward environment and infrastructure issues and turn findings into targeted research into Apple experiences. Collaborate with software and hardware teams to integrate and validate agent capabilities under realistic on-device external research impact. Contribute to research projects from initial hypothesis through experimental validation publish high-impact technical reports as a core author and release open-source models that advance agentic post-training and support the broader research community. Example technical reports: Internalized Visual Thinking for Video Reasoning Structured Reasoning for Efficient Agentic Search.

MS in Computer Science Machine Learning Artificial Intelligence Computer Vision Natural Language Processing or a related field or equivalent practical strong research track record demonstrated by either first-author publications at top-tier conferences (such as NeurIPS ICML ICLR CVPR ICCV ECCV ACL or EMNLP) or substantial hands-on experience training frontier models with contributions relevant to foundation models reinforcement learning multimodal learning or agentic programming skills in Python and proficiency with deep learning frameworks such as ability to formulate research hypotheses implement training and evaluation pipelines conduct controlled experiments and draw well-supported conclusions.

PhD in Computer Science Machine Learning Artificial Intelligence Computer Vision Natural Language Processing or a related field or equivalent practical experience. Research focus on foundation models reinforcement learning agentic systems multimodal reasoning or efficient machine contributions to high-impact technical reports or open-source model releases in agentic post-training with substantial ownership of research methodology model training experimental validation and technical developing or training agents for tool use and long-horizon designing interactive training environments trajectory-generation pipelines reward models or scalable agent improving smaller models through compute-aware post-training with an understanding of on-device latency memory and energy to independently drive research projects and collaborate effectively across research and engineering teams.