Software Engineering Manager, LLM Training

LinkedIn•Mountain View, CA

1d•Hybrid

About The Position

As a Software Engineering Manager of the Post-Training Infra team, you will architect the high-throughput systems required for Supervised Fine-Tuning (SFT) and RL, Multi-Techer Distillation, Reinforcement Learning from Human Feedback (RLHF), Agentic Performance Optimization and Agentic Research at scale. You won’t just be "running scripts"; you’ll be optimizing the engine that makes rapid model alignment possible.

Requirements

BA/BS Degree in Computer Science or related technical discipline, or equivalent practical experience.
1+ year(s) of management experience or 1+ year(s) of staff level engineering experience with management training
5+ years of industry experience in software design, development, and large-scale software engineering
Experience in LLMs - Post Training and/or Inference for a year minimum
Hands on experience developing distributed system

Nice To Haves

MS or PhD in Computer Science or related technical discipline
2+ years of hands-on software engineering/technical management and people management experience
7+ years industry experience in software design, development, and algorithm related solutions.
Experience in architecting, building, and running large-scale distributed systems
Experience with industry, opensource, and/or academic research research papers published in the space

Responsibilities

Enable and support sophisticated parallelism strategies, including data, tensor, pipeline, context, and expert parallelism, for models exceeding 100B+ parameters. Provide optimized configurations, reference examples, and platform-level integration so that customer teams can effectively leverage these techniques
Maintain deep expertise across the post-training landscape, including Multi-Teacher Distillation, RL-based alignment and optimization (RLHF, GRPO), Pruning, Quantization, and Speculative Decoding. Build and maintain reusable platform components that enable customer teams to efficiently leverage these techniques in their workflows.
Deep-dive into strategic customer workloads and drive workload-specific and platform-level optimizations, including Liger Kernels, FlashAttention, low-precision training, high-performance data I/O, and inter-node latency reduction.
Video and Audio Models Post Training strategy
Act as a bridge to the OSS community. You will contribute to and troubleshoot the "Post-Training Stack," including Liger, PyTorch, Hugging Face (Accelerate/Transformers), Megatron, Ray, VERL, SGLang and vLLM.
Develop advanced telemetry for large-scale training runs. You will use profiling tools to debug hardware-level stalls (NCCL timeouts, memory fragmentation) and provide internal teams with actionable insights into training stability.
Lead the development of the "Golden Image" environment. Maintain and distribute optimized, containerized base images with compatible, validated builds of PyTorch, CUDA, and the broader training stack to ensure seamless training on our clusters.
Serve as the bridge between the training platform and Responsible AI teams, collaborating on data compliance, model evaluation, and safety processes. Ensure the platform provides the tooling and integration points needed for RAI teams to effectively apply their frameworks throughout the training lifecycle.
Lead development of Agents for autonomous model research, performance optimization
Lead, coach and manage core team of engineers working on building the infrastructure.
Participate with senior management in developing a long-term technology roadmap for the team and company.
Have the ability to dive deep into technical discussions to challenge the status quo, and steer the team in the right direction/to push the envelope.
Communicate and collaborate effectively with stakeholders across engineering and business leadership.
Help the team realize their potential by setting clear expectations, openly evaluating performance, upholding accountability, and providing challenges to stretch their skills.
Drive a culture of operational excellence. Lead the team into defining performance goals, metrics and building the infrastructure and tooling necessary to maintain a high quality bar and detect issues in real time.
Create an inclusive work environment that fosters autonomy, transparency, innovation and learning, while holding a high bar for quality.