Machine Learning Engineering Technical Leader

Cisco’s Splunk is hiring a Machine Learning Engineering Technical Leader for its Code Generation group. The role leads research and production deployment of AI systems for intelligent code generation, advances agentic GenAI and DSL-aware code synthesis, and guides distributed training and inference. It also includes collaboration with engineering and product teams, research-team mentorship, and shaping AI strategy.

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 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

Requirements:

• 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
• 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)
• Background building generative models that target structured languages (e.g., SQL, DSLs, configuration languages, or proprietary query languages/SPL)
• Experience designing agents that plan, call tools/APIs, self-reflect, or drive code to iteratively refine solutions
• Proven success applying techniques such as chain-of-thought, self-debugging, constrained decoding, or reinforcement learning for software development tasks
• Experience with distributed training, efficient inference (quantization, LoRA, caching, batching), and cost-aware scaling
• Familiarity with automated model retraining, dataset curation, synthetic data pipelines, eval harnesses, and model health monitoring
• Publications in premier AI/ML venues (NeurIPS, ICML, ICLR, ACL, AAAI, KDD, etc.) and/or recognized contributions in the code-gen / LLM community