below marketmost similar roles pay hereabove market
This role pays more than
73%
of similar roles. Most pay
$174,950–$215,000
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At the midpoint, this role pays about
$213k
versus about
$195k
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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
1655 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 1061 roles with salary data.
The Senior Technical Program Manager - LLM Safety joins the team to lead cross-functional planning and execution for evaluation identification, selection, and execution. This role focuses on agentic-safety evaluations, including multi-turn tool calling, unsafe tool use, and unintended actions. You will translate technical safety priorities into executable programs covering LLM security, frontier risks, and hallucinations for Nemotron models. Responsibilities include establishing governance for reviewing safety findings, tracking multi-modal and multi-lingual evaluations, and building dashboards for executive reporting. The role requires expertise in LLM architecture, frameworks like OpenAI, Anthropic, and Hugging Face, and experience with post-training, inference, and Red Teaming. You will solve critical problems regarding data poisoning, model manipulation, and ensuring required safety evidence and risk-acceptance decisions are documented before model release.
What does a Technical Program Manager earn in California?
Median $199350 from 104 postings across 24 companies.
Lead cross-functional planning and execution for identifying, selecting, and executing agentic-safety evaluations.
Track evaluation results to develop analysis and research plans for model improvements.
Translate evaluation findings into prioritized mitigation plans with accountable owners and measurable closure criteria.
Manage end-to-end safety planning for Nemotron models, including scope, dependencies, risks, and release-readiness.
Establish and track multi-turn, multi-modal, and multi-lingual evaluations across various domains and model releases.
Govern the review of safety findings by assigning severity, determining release impact, and escalating unresolved risks.
Document all required safety evidence, approvals, exceptions, and risk-acceptance decisions prior to model release.
Build and maintain dashboards for executive reporting on evaluation coverage, critical findings, and mitigation progress.
What we're looking for
Bachelor’s degree or equivalent experience in computer science, engineering, data science, or a related technical field.
10+ years of experience in technical program management, engineering, product development, technical operations, or a similar area.
Strong understanding of LLM architecture, frameworks, and model evaluation.
Familiarity with LLM development, post-training, inference, tool calling, evaluation datasets, and model-release lifecycles.
Experience directing programs related to AI/ML development, model evaluation, agentic safety, security, content safety, hallucinations, and/or production releases.
Familiarity with AI safety risks such as hallucinations, timely injection attacks, unsafe tool use, data poisoning, model manipulation, and unintended agent behavior.
Experience supporting model-safety evaluations, Red Teaming, adversarial testing, security assessments, or responsible-AI programs.
Ability to interpret technical evaluation findings and communicate their product, schedule, and release implications to leadership.
LLM
AI
Red Teaming
Adversarial Testing
Hugging Face
OpenAI
Anthropic
Model Evaluation
Agentic Safety
Content Safety
Inference
Post-training
Data Poisoning
Security Assessment
Responsible AI
Multi-modal
Multi-lingual
Long-context
Hardware Development
Cloud Infrastructure
Technical Program Management
datacenter operations
Networking
Supply Chain
Risk Management
Product Development
Optics Switch Systems
Data Analysis
Debugging