Note: The job is a remote job and is open to candidates in USA. NVIDIA develops AI-based products across multiple domains and collaborates with leading AI companies as partners and customers. The Senior Machine Learning Engineer will develop and deploy datasets, models, algorithms, and training approaches to improve the security, content safety, inclusivity, and reliability of multimodal large language models, including agentic systems. The role also involves researching advanced AI safety techniques and collaborating cross-functionally with engineering, data science, and research teams.
Responsibilities
- Evaluation: Develop datasets and specialized models & algorithms to evaluate/benchmark models & end-to-end systems in our core safety tracks (LLM Security, Agentic Safety, Content Safety, Hallucinations, and ML Fairness)
- Model Pre-Training, Mid-Training, Post-Training: Develop datasets and recipes for filtering training data, developing training datasets & recipes, including components like RL environments and teacher models, across our core safety tracks (LLM Security, Agentic Safety, Content Safety, Hallucinations, and ML Fairness)
- Model & system level techniques beyond post-training: Research & deploy new approaches, like Instruction Hierarchy or Risk Detection
- Cross-Functional Collaboration: Partner with engineers, data scientists, and research teams across NVIDIA to scale solutions for LLM Security, Agentic Safety, Content Safety, Hallucinations, and ML Fairness
Skills
- Master's or PhD in Computer Science, Electrical Engineering, or a related quantitative field (or equivalent experience)
- 8+ years of proven experience in systems software engineering or machine learning engineering
- 4+ years of hands-on work experience in post-training of LLMs, including Supervised Fine-Tuning (SFT), Reinforcement Learning (RLHF/RLAIF), safety data generation techniques, ablation studies, and deploying models to production
- 1+ years of dedicated experience or research in at least one of the following areas:
- LLM Security (backdoors, poisoning, latent behaviors)
- Frontier Risks (deception, manipulation, loss-of-control)
- Agentic Safety (LLM-level risks for multi-turn tool-calling/agents)
- Multi-turn Safety Evaluation (dynamic and multi-turn alignment benchmarks)
- In-depth knowledge of machine learning principles and frameworks (PyTorch preferred) with strong Python programming skills
- Experience working with large multimodal datasets and multi-modal foundational models
- Outstanding analytical problem-solving abilities paired with excellent collaboration and communication skills
- Demonstrates behaviors that build trust: humility, transparency, respect, and intellectual honesty
- Published papers on AI Safety, alignment, or machine learning security as a primary author at top-tier conferences (NeurIPS, ICML, ICLR, ACL, etc.)
- Active contributions to open-source AI Safety tools, benchmarks, datasets, and/or models
- Proven experience with alignment/fine-tuning of Vision-Language Models (VLMs) or any-to-text foundational models
Benefits
- You will also be eligible for equity
- A comprehensive benefits package
Company Overview
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