1 day ago
Menlo Park, CA, USA or New York, NY, USAMid Level
H1B sponsor
Base Salary
$184k - $257k/yr
Responsibilities
- Research and develop machine learning models and algorithms for AI safety, guardrails, risk detection, policy adherence, and adversarial robustness.
- Design and implement safety systems for language models, multimodal models, and AI agents, including defenses against harmful assistance, jailbreaks, prompt injection, unsafe tool use, and unauthorized information disclosure.
- Set technical direction and lead complex safety initiatives across research, alignment, evaluation, product, and infrastructure teams.
- Architect and optimize reusable safety components and machine learning pipelines with learned detection and runtime controls for scalable, low-latency production safeguards.
- Generate training data, synthetic and adversarial examples, and benign cases; conduct experimentation, ablation studies, and held-out evaluations.
- Measure deployed safeguard effectiveness while balancing risk reduction, false refusals, task completion, privacy, latency, cost, and reliability.
- Collaborate with technical, product, evaluation, security, and policy partners to deliver validated production solutions.
- Monitor and maintain deployed safety models, identify failure patterns using privacy-preserving workflows, create regression tests, and drive system improvements.
- Track developments in AI safety, LLMs, multimodal modeling, adversarial robustness, and agentic systems; mentor engineers and researchers and communicate findings.
Requirements
- Bachelor’s degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
- Research track record in AI safety, deep learning, adversarial robustness, language models, multimodal machine learning, or agentic AI, supported by original or materially improved methods and rigorous experimental evidence.
- At least 3 years of experience deploying machine learning models and systems at scale in an industry setting.
- Current expertise in relevant machine learning research, including reproducing, evaluating, and adapting emerging methods to real-world systems.
- Hands-on Python and PyTorch experience developing, training or fine-tuning, debugging, and evaluating machine learning models.
- Experience independently defining technical direction and leading complex research or machine learning initiatives end-to-end across multiple teams.
- Ability to communicate complex technical concepts, influence cross-functional decisions, and mentor engineers or researchers.
- Preferred qualifications include an advanced degree or equivalent advanced research experience, AI safety guardrail or adversarial defense experience, safety data generation and evaluation expertise, agent safety and prompt-injection defense experience, strong research contributions, and experience with efficient inference, distillation, cascaded models, or distributed ML systems.
Benefits
- Bonus, equity, and benefits are included in the compensation package.
Categories
AI ResearchML Engineering
About Meta
Meta builds social platforms and communication apps—including Facebook, Instagram, WhatsApp, and Messenger—and develops AR/VR hardware and software such as Quest to power immersive computing. It monetizes primarily through advertising tools for businesses, with additional revenue from devices and services, and operates a massive global infrastructure. Founded in 2004 and headquartered in Menlo Park, California, Meta Platforms, Inc. is a public company traded on Nasdaq under the ticker META.