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# Machine Learning Engineering Technical Leader

- **Company:** Cisco Systems, Inc.
- **Location:** Seattle, WA, United States
- **Experience:** Expert
- **Salary:** $234,400.0 - $296,600.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Unit Testing, Code Generation, Query Languages, Distributed Computing Environment, Dynamic Program Analysis, Python (Programming Language), Machine Learning, Tensorflow, Software Engineering, SQL Databases, Reinforcement Learning, Pytorch, Large Language Models, Deep Learning, Generative AI, Agentic-AI, Discretization, Information Technology, Machine Learning Operations, Code Restructuring, GPT, Data Pipelines
- **Published:** October 26, 2026
- **Apply:** https://www.seattlejobs.com/job.asp?id=3468153006&tx=ZT2726THI&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field and 5+ years of post-doctoral or industry experience; or a Master's degree in a related field and 10+ years of industry experience.
 * 3+ years experience in developing Large Language Models (LLMs) for program synthesis or formal languages
 * 2+ years experience in Multi-step planning or agentic AI for developer workflows
 * 3+ years experience with Python and deep learning frameworks such as PyTorch or TensorFlow.
 * 3+ years experience translating research prototypes into production systems, including model deployment, optimization, and evaluation., * Strong foundation in experimental design, benchmarking, reproducibility, evaluation metrics, and scientific documentation.
 * LLMs for Code Generation - Experience with training, fine-tuning, or adapting models such as Code-LLaMA, CodeT5, StarCoder, or GPT-based code models for program synthesis, refactoring, unit test generation, static/dynamic analysis, or domain-specific languages (DSLs).
 * Domain-Specialized Modeling - Background building generative models that target structured languages (e.g., SQL, DSLs, configuration languages, or proprietary query languages/SPL).
 * Agentic AI & Tool Use - Experience designing agents that plan, call tools/APIs, self-reflect, or drive code to iteratively refine solutions.
 * Structured Reasoning & Planning - Proven success applying techniques such as chain-of-thought, self-debugging, constrained decoding, or reinforcement learning for software development tasks.
 * Large-Scale Training & Optimization - Experience with distributed training, efficient inference (quantization, LoRA, caching, batching), and cost-aware scaling.
 * MLOps & Continuous Evaluation - Familiarity with automated model retraining, dataset curation, synthetic data pipelines, eval harnesses, and model health monitoring.
 * Research Leadership - Publications in premier AI/ML venues (NeurIPS, ICML, ICLR, ACL, AAAI, KDD, etc.) and/or recognized contributions in the code-gen / LLM community.

## Description

* Own the full lifecycle of research and deployment of next-generation AI systems for intelligent code generation, including model design, evaluation, and production rollout.
 * Define the scientific roadmap for agentic GenAI, enabling models that not only generate code but reason, plan, self-correct, and integrate with tools and runtime environments.
 * Advance the state of the art in DSL-aware code synthesis, shaping how future developer experiences are powered by LLMs across interactive and automated workflows.
 * Drive efficiency and scalability of distributed training and inference pipelines to balance performance, latency, and cost - without compromising accuracy or reliability.
 * Collaborate closely with engineering and product to ensure AI breakthroughs translate quickly and safely into high-impact customer capabilities.
 * Mentor and elevate a high-performing research organization, fostering a culture of scientific rigor, creativity, and delivery excellence.
 * Shape long-term AI strategy and innovation, influencing architectural decisions, technical investments, and roadmap direction across the GenAI organization.