below marketmost similar roles pay hereabove market
This role pays more than
99%
of similar roles. Most pay
$156,587–$213,200
— the blue band above.
At the midpoint, this role pays about
$352k
versus about
$185k
for comparable roles.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
1623 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 1110 roles with salary data.
The Senior Manager, Software Engineering - RL Post-Training Frameworks leads the RL Frameworks engineering team to build and harden open-source tools and infrastructure for reinforcement learning post-training. You will own the strategy for RL post-training frameworks, converting technical and customer signals into engineering priorities while managing a globally distributed team of managers and senior individual contributors. Daily responsibilities include evaluating architecture and performance across training, inference, and orchestration, while establishing benchmarking and reproducibility criteria. You will work with frameworks such as VeRL, Miles, Slime, SkyRL, TorchTitan, Megatron-Core, Ray, Monarch, NIXL, SGLang, and Kubernetes. This role solves the complex systems problem of scaling RL loops reliably across GPUs, CPUs, networking, and storage to enable modern AI systems to reason, use tools, and follow instructions.
What does a Software Engineering Manager earn in California?
Median $281300 from 43 postings across 15 companies.
Own the strategy for RL post-training frameworks including investment decisions and technical prioritization.
Evaluate architecture and performance claims across training, inference, rollout, and orchestration systems.
Convert customer and partner signals into clear engineering priorities and measurable execution plans.
Recruit, develop, and manage a global team of managers and senior individual contributors.
Drive technical integrations across organizational boundaries to improve RL framework quality and user value.
Establish benchmarking, reproducibility criteria, and success metrics for distributed runtimes.
Coach engineers to contribute high-quality upstream work to open-source ecosystems.
Set delivery goals and hold the quality bar for production-ready reinforcement learning systems.
What we're looking for
MS or PhD in Computer Science, Computer Engineering, or a related field (or equivalent experience).
10+ years of software engineering experience in distributed systems, AI frameworks, ML infrastructure, high-performance computing, or systems software.
4+ years of experience as an engineering manager for software teams.
Strong technical background in distributed AI systems, including training, inference, orchestration, and end-to-end performance.
Experience defining domain-level technical strategy, making build-vs-buy decisions, and creating multi-team execution plans.
Experience hiring and leading engineering teams, developing technical leaders, and creating staffing plans.
Experience collaborating with open-source communities, research teams, external partners, or customer-facing teams.
Hands-on experience with RL post-training frameworks or algorithms such as RLHF, PPO, GRPO, DPO, or TorchTitan (preferred).
Remote (Santa Clara, CA)77 days ago
$320,000–$488,750
GPU Architecture
CUDA
TensorRT-LLM
vLLM
SGLang
Ray
Kubernetes
Linux
Distributed Systems
Model Serving
Hardware Acceleration
Cloud-Native Architecture
High-Performance Computing
Dynamo