Research Engineer - AI Safety Systems and Guardrails

Responsibilities

• Research and develop state-of-the-art machine learning models and novel algorithms for AI safety, with a primary focus on guardrails, risk detection, policy adherence, and adversarial robustness
• Design, prototype, and implement safety systems for language models, multimodal models, and AI agents, addressing harmful assistance, jailbreaks, prompt injection, unsafe tool use, and unauthorized information disclosure across individual interactions and complete agent tasks
• Set the technical direction and research roadmap for a significant system safety area, leading complex initiatives across research, alignment, evaluation, product, and infrastructure teams
• Architect and optimize reusable safety components and machine learning pipelines, combining learned detection with explicit runtime controls and delivering scalable, low-latency safeguards for real-world applications
• Develop high-quality training data, synthetic and adversarial examples, and representative benign cases; conduct rigorous experimentation, ablation studies, and independent held-out evaluation to improve safety, generalization, and robustness
• Measure the effectiveness of safeguards within the fully deployed system, balancing risk reduction with false refusals, legitimate task completion, privacy, latency, cost, and reliability
• Collaborate closely with model researchers, product managers, engineers, and evaluation, security, and policy partners to translate product needs and safety requirements into validated, production-ready solutions
• Monitor, maintain, and iterate on deployed safety models and systems, using approved data and privacy-preserving workflows to identify emerging failure patterns, create regression tests, and drive lasting system or model improvements
• Stay at the forefront of research and industry trends in AI safety, LLMs, multimodal modeling, adversarial robustness, and agentic systems; reproduce, critically evaluate, and extend promising methods for product deployment. Mentor engineers and researchers to influence architecture and research decisions, and communicate findings through technical documentation, internal presentations, and external publications where appropriate

Minimum Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• Proven track record of research in one or more of the following areas: AI safety, deep learning, adversarial robustness, language models, multimodal machine learning, or agentic AI, demonstrated through original or materially improved methods and rigorous experimental evidence
• 3+ years of experience in deploying machine learning models and systems at scale in an industry setting
• Demonstrated up-to-date expertise in relevant machine learning research advancements, including the ability to reproduce, critically evaluate, and adapt emerging methods to real-world systems
• Hands-on programming experience in Python and deep-learning frameworks such as PyTorch, including developing, training or fine-tuning, debugging, and evaluating machine learning models
• Demonstrated experience independently defining technical direction and leading complex research or machine learning initiatives end-to-end, aligning multiple teams and delivering measurable product or platform impact
• Proven ability to communicate complex technical concepts, influence cross-functional technical decisions, and mentor engineers or researchers

Preferred Qualifications

• Experience developing AI safety guardrails, policy-adaptable classifiers, reliable model-based judges, or adversarial defenses for deployed AI systems
• Advanced degree (MS or PhD) in Computer Science, Machine Learning, AI, or a related technical field, or equivalent advanced research experience
• Expertise in safety data generation, adversarial evaluation, calibration, or in multilingual and multimodal safety modeling
• Experience with agent safety, prompt-injection defense, multi-turn risk detection, tool-use controls, or security-informed threat modeling
• A strong record of relevant research contributions, such as peer-reviewed publications, adopted open-source methods, or widely used systems supported by rigorous evaluation
• Experience with efficient model inference, distillation, cascaded models, or distributed ML systems under latency, throughput, cost, and reliability constraints

Compensation

• $183,997/year - $257,000/year; Country: US; Bonus eligible; Equity eligible

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