Senior Software Engineer - LLMs, Agent Systems, and Simulation Tooling
Description
We’re looking for a senior software engineer with a strong proficiency in ML systems and large language models to help build advanced reasoning capabilities for the Siri virtual assistant. We are a collaborative team of ML engineers, scientists, and software developers passionate about LLM planner modeling to build the next generation of the Siri virtual assistant. Your work will center on developing reliable, scalable tooling for training, simulation, and evaluation of LLMs and agentic systems. You’ll also play a hands-on role in designing and running experiments in simulation, and integrating client-side components with backend systems into a production-ready testable environment. This is an opportunity to work with pioneering technologies that push the boundaries of LLM agent reasoning, system architecture, and rapid iteration. You’ll collaborate closely with ML developers, client-side engineers, and deep learning leaders to bring enhanced LLM reasoning capabilities into production.
Minimum Qualifications
Bachelor's degree in Computer Science or related quantitative field, with at least 4+ years of relevant industry experience with the following: Strong skills in Python (desired) and at least one other object-oriented programming language Proven experience in software engineering, including system design, development, testing, debugging, release and maintenance In-depth understanding of agent-based simulation frameworks, Agentic RAG systems, prompt engineering, and evaluation best practices for LLMs Ability to develop long-term strategic visions and implement scalable solutions in fast-paced, agile work environments
Preferred Qualifications
Experience supporting ML teams or deploying LLM models in production or research Knowledge of feedback mechanisms and adaptive agent behaviors Experience integrating client-side applications or code with backend systems (APIs or custom protocols) MS or advanced degree in Computer Science, Machine Learning or related quantitative filed is preferred
Average salary estimate
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