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

Apple, Inc.; Sunnyvale (CA); In loco; USD 150.000 - 278.000; Tempo pieno

Vantaggi offerti da questo lavoro

Stock options

Medical and dental coverage

Tuition reimbursement

Relocation assistance

Descrizione del lavoro

Apple, Inc. is seeking a Machine Learning Researcher focused on Agentic Post-Training for on-device models in Sunnyvale, CA.

The role emphasizes developing methods to enable smaller models to reason, plan, and use tools efficiently within tight latency and energy budgets, with strong emphasis on privacy-preserving inference. The team works across hardware, software, and ML disciplines and expects leadership in end-to-end project execution, rigorous experimentation, and publication of results.

Competenze

• Strong programming in Python and proficiency with deep learning frameworks such as PyTorch.
• Demonstrated ability to formulate research hypotheses, implement training and evaluation pipelines, conduct controlled experiments, and draw well-supported conclusions.
• Experience with foundation models, reinforcement learning, multimodal learning, or agentic systems is valued.

Mansioni

• 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 LLMs.
• Design training data and interactive environments. Build scalable pipelines for trajectory generation, curriculum construction, and hard-example mining.
• Design reward functions, verifiers, and graders for complex agent tasks, addressing long-horizon credit assignment and gaps between reward scores and task success.
• Improve capability under deployment constraints, balancing task success with latency, memory, energy, and tool-use costs.

Conoscenze

Python

PyTorch

Research

Reinforcement learning

Formazione

MS in Computer Science, ML, AI, CV, NLP, or related field

PhD in related field (preferred)

Descrizione del lavoro

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

Sunnyvale, California, United States Machine Learning and AI

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.

Description

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.

Responsibilities

• 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 LLMs.
• Design 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 interaction.
• Design reward functions, verifiers, and graders for complex agent tasks, addressing long-horizon credit assignment, supervision quality, and gaps between reward scores and actual task success.
• Improve 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 computation.
• Build 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 improvements.
• Translate research into Apple experiences. Collaborate with software and hardware teams to integrate and validate agent capabilities under realistic on-device constraints.
• Drive 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.”

Minimum Qualifications

• MS in Computer Science, Machine Learning, Artificial Intelligence, Computer Vision, Natural Language Processing, or a related field, or equivalent practical experience.
• A 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 systems.
• Strong programming skills in Python and proficiency with deep learning frameworks such as PyTorch.
• Demonstrated ability to formulate research hypotheses, implement training and evaluation pipelines, conduct controlled experiments, and draw well-supported conclusions.

Preferred Qualifications

• 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 learning.
• Significant 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 communication.
• Experience developing or training agents for tool use and long-horizon tasks.
• Experience designing interactive training environments, trajectory‑generation pipelines, reward models, or scalable agent evaluations.
• Experience improving smaller models through compute‑aware post‑training, with an understanding of on‑device latency, memory, and energy constraints.
• Ability to independently drive research projects and collaborate effectively across research and engineering teams.

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $150,400 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant

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Apple accepts applications to this posting on an ongoing basis.