Machine Learning Engineering Technical Leader

Cisco•Seattle, WA

17h•$234,400 - $341,100

About The Position

Splunk, a Cisco company, is building a safer, more resilient digital world with an end-to-end, full-stack platform designed for hybrid, multi-cloud environments. Join the Code Generation group, where we work on automating the code generation process using GenAI techniques. We combine deep AI research expertise with the scale and operational excellence of Splunk and Cisco’s global engineering capabilities. Our work spans networking, security, observability, and customer experience, designing and deploying foundation models that enhance reliability, strengthen security, prevent downtime, and deliver predictive insights across Splunk Observability, Security, and Platform at enterprise scale. You’ll be part of a culture that values technical excellence, impact-driven innovation, and cross-functional collaboration, all within a flexible, growth-oriented environment.

Requirements

PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field and 5+ years of post-doctoral or industry research experience; or a Master’s degree in a related field and 10+ years of progressively responsible research 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.

Nice To Haves

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 execute 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 code-oriented 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.

Responsibilities

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 automation-driven 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.

Benefits

medical, dental and vision insurance
a 401(k) plan with a Cisco matching contribution
paid parental leave
short and long-term disability coverage
basic life insurance
10 paid holidays per full calendar year
1 floating holiday for non-exempt employees
1 paid day off for employee’s birthday
paid year-end holiday shutdown
4 paid days off for personal wellness
16 days of paid vacation time per full calendar year (non-exempt)
flexible vacation time off program (exempt)
80 hours of sick time off provided on hire date and each January 1st thereafter
up to 80 hours of unused sick time carried forward from one calendar year to the next
optional 10 paid days per full calendar year to volunteer
annual bonuses (non-sales roles)
performance-based incentive pay (sales roles)
Cisco restricted stock units