Machine Learning Engineer - Agentic AI

Apple

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About The Position

As a member of the ML group, the Machine Learning Engineer will design, prototype, and ship agentic generative AI systems that help chip design engineers accelerate their day-to-day workflows. The role focuses on turning LLM demonstrations into dependable tools by designing agent loops, retrieval pipelines, and evaluation frameworks, with success measured by meaningful improvements to customer workflows and trusted performance measurements.

The engineer will join a growing team of ML and software engineers building GenAI solutions for chip design teams. The role involves collaborating with customer-team subject-matter experts, software engineers, researchers, and customers to create and productionize agentic solutions, and bringing useful advances in the agentic and LLM landscape into products with sound engineering judgment.

Requirements

• At least 3 years of applied ML/AI experience, including hands-on work building LLM-powered or agentic applications.
• Strong programming skills in Python.
• Experience with modern machine learning and deep learning frameworks, such as PyTorch.
• Hands-on experience with agentic frameworks, tool use, and data pipelines.
• Ability to design golden sets, metrics, and telemetry to drive iteration.
• Excellent communication and collaboration skills, including the ability to align with subject-matter experts and translate domain knowledge into agent behavior.
• Hands-on experience with stateful, multi-turn agentic frameworks and complex execution flows.
• Experience with prompt engineering and optimization techniques.

Nice To Haves

• Experience with model adaptation, such as fine-tuning, distillation, or preference optimization.
• Publications at top-tier venues such as NeurIPS, ICML, ICLR, ACL, EMNLP, or similar.
• Familiarity with chip design or EDA environments.

Responsibilities

• Design and build agentic systems featuring multi-agent coordination, long-horizon reasoning, tool use, and planning frameworks.
• Develop end-to-end solutions, including domain-specific agents and high-performance retrieval pipelines spanning diverse data sources.
• Partner with customer-team subject-matter experts to translate workflows and rules of thumb into agent assignments, prompts, tools, and guidelines.
• Build the evaluation backbone for agents by defining success measures, curating offline golden sets, and instrumenting online telemetry for feedback loops and safe iteration with cost and quality control.
• Drive prompt engineering, model selection, and lightweight model adaptation as workloads demand.
• Collaborate with software engineers to productionize work, and with researchers and customers to shape future development.
• Stay current with the agentic and LLM landscape and bring promising techniques into products with good engineering judgment.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed