AI safety Jobs

The Imagine team is redefining AI-driven media experiences for Grok users worldwide. As a Product Safety Engineer on the Imagine team, you will build the critical safety systems and infrastructure that ensure Grok’s multimodal generation capabilities (images, video, audio, and beyond) are powerful, delightful, and responsibly deployed. You’ll design and scale safeguards, detection systems, and evaluation frameworks that prevent harm while preserving creativity and user freedom. Your work will directly shape how millions of users experience generative media — balancing rapid innovation with rigorous safety, trustworthiness, and alignment with SpaceXSI's mission. If you’re a driven engineer passionate about AI safety, content integrity, and building robust systems that protect users without stifling innovation, this is your opportunity to make a lasting impact at the frontier of multimodal AI.

$180k - $440k

Anthropic

Anthropic is seeking individuals for a paid, fixed-term research residency program, lasting between six weeks and six months. This program is designed for experienced professionals, such as professors in quantitative disciplines, technical founders, or staff-level engineers, who are looking to transition into full-time work focused on ensuring AI systems are safe and beneficial for humanity. While a background in machine learning is not strictly required, candidates should be motivated by AI safety and excited about empirical AI research. The residency offers a scoped project with direct mentorship from Anthropic researchers, access to Claude models and GPU compute, and connections to the AI safety and security research community.

$350k - $500k

Raindrop is seeking a founding Research Engineer to investigate the behavior of AI agents in production environments. This role is crucial for an early-stage team, involving setting the research direction, conducting experiments, and disseminating findings through publications. The focus will be on understanding agent failures, advancing simulation techniques, developing novel detection methods for undefined issues, and exploring high-impact open questions in AI agent behavior. The ideal candidate will possess strong communication skills, a passion for sharing research, deep expertise in areas like interpretability or AI safety, and the ability to prototype and ship experiments.

OpenAI

OpenAI's Safety Research organization is seeking a senior researcher to lead technical work on misalignment monitoring for frontier AI systems. This role involves developing methods to evaluate AI monitors, understanding their limitations, and improving our ability to reliably detect and stop concerning model behavior. You will set research priorities, design rigorous evaluations, investigate monitoring failures, and develop approaches for stress-testing and improving monitoring systems, with a key focus on understanding monitor limitations as models evolve. The position combines research leadership with hands-on technical execution, translating research findings into practical improvements for OpenAI's safety systems.

$380k - $500k

Scale AI

Scale Labs is seeking a Research Scientist focused on Safety Post-Training to develop and apply methods for making frontier AI systems safer and more understandable. This role involves designing and executing post-training pipelines to analyze how training choices impact model safety, robustness, and alignment. You will also develop evaluations informed by interpretability techniques to uncover the reasons behind undesirable model behaviors and use these insights to guide targeted safety mitigations. A key aspect of this position is collaborating with policymakers, engineers, and other researchers to translate findings into actionable safety standards, benchmarks, and best practices.

$216k - $270k

Anthropic

Anthropic's mission is to create reliable, interpretable, and steerable AI systems that are safe and beneficial for users and society. The Interpretability team focuses on mechanistic interpretability, aiming to discover how neural network parameters map to meaningful algorithms. This involves reverse-engineering how trained models work, treating neural networks like biological systems or computer programs to be reverse-engineered. The team's goal is to create a solid scientific foundation for understanding neural networks and making them safe, with a focus on issues like "superposition" and decomposing models into interpretable components. This role supports a team of expert researchers and engineers working on these ambitious safety research goals.

$350k - $500k

NVIDIA Halos is seeking a Senior Software Safety Engineer to join their team, focusing on the full-stack, comprehensive safety system for Physical AI. This role involves ensuring the safe development and deployment of autonomous machines, AI models, chips, software, tools, and services. You will collaborate with diverse engineering teams across NVIDIA, including those working on autonomous vehicles, robotics, and other safety-critical applications. The position requires guiding teams in specifying, building, implementing, and verifying safety requirements across the software stack, from drivers to end-to-end AI models, and integrating AI safety principles into the Product Life Cycle. Additionally, you will engage in customer interactions and international standardization activities related to AI safety, and support system safety activities like decomposition of safety requirements and architectural safety analysis.

$224k - $357k

Synthesia is seeking a Staff Research Scientist to join their R&D department, focusing on the frontier of generative AI, specifically avatar-centric, interactive video diffusion models. This role involves building AI models capable of natural, real-time conversations, understanding user input (audio and video), and generating appropriate human-like reactions. The team operates in a fast-paced environment, translating cutting-edge research into tangible product features. This is a unique opportunity for experts in machine learning and diffusion models to significantly influence the future of AI video agents.

Abundant is an applied research lab focused on scaling reinforcement learning for safe and reliable agentic capabilities. As a Member of Technical Staff, Alignment, you will be instrumental in ensuring that alignment is a core component of all our work, from the code we deploy and the runbooks we write to the papers we publish. This role involves testing models in advanced environments, identifying and mitigating potential risks like reward hacking and deception, and building robust tools to monitor and secure AI agents.

$250k - $450k

Abundant is building the NVIDIA of training data, recognizing the critical need for scaling data as AI models advance beyond supervised learning to reinforcement learning and learning from experience. The founding team brings extensive experience from leading AI organizations, with a proven track record in deploying deep learning models at scale, training state-of-the-art models, and managing large data pipelines. As a Member of Technical Staff, you will collaborate directly with the founding team to architect the future of model reasoning and intelligence, working at the intersection of research and execution to push the boundaries of AI capabilities.

$120k - $120k

Anthropic

The Safeguards ML Inference Path team designs, builds, and operates the production infrastructure that powers Claude's ML-based safety systems. This role involves collaborating closely with safety researchers and inference engineers to bring new classifiers and novel classes of ML defenses to production, owning the research-to-production transfer of new safety technologies critical for every Claude model launch. The team builds for scale, serving thousands of ML classifiers for all requests on the token generation path and for every platform Claude runs on. Engineers will work at the intersection of machine learning, large-scale distributed systems, and AI safety, developing platforms and tools that enable safeguards to operate reliably at scale, with tooling and infrastructure used for every model launch.

$320k - $485k

Decagon is seeking a Research Engineer focused on Safety to ensure the reliability and controllability of their AI agents. This role involves identifying real-world failure modes and developing the necessary models, evaluations, and safeguards to prevent them. The ideal candidate will be a strong engineer passionate about advancing applied AI safety in production, with the ability to own projects end-to-end and make high-impact technical decisions.

$200k - $400k

Decagon is seeking a Senior Research Engineer focused on Safety to ensure the reliability and controllability of their AI agents. This role involves identifying real-world failure modes and developing the necessary models, evaluations, and safeguards to prevent them. The ideal candidate will be a strong engineer passionate about advancing applied AI safety in production, taking ownership of their work from end-to-end, and making high-impact technical decisions.

$200k - $400k

OpenAI

This role focuses on oversight and system-level mitigations to enable increasingly capable AI agents to operate safely and autonomously in real environments. The team prioritizes building oversight systems used in practice today, both internally and externally, and studies longer-term questions about how agent systems can be supervised, constrained, and corrected. We are looking for a safety-minded researcher or engineer who can reason rigorously about security boundaries and agent behavior, then build and test practical mitigations. A background in AI control or security is welcome but not required.

$380k - $500k

OpenAI

OpenAI is seeking a researcher passionate about understanding deep networks, with a strong background in engineering, quantitative reasoning, and the research process. You will develop and carry out a research plan in mechanistic interpretability, in close collaboration with a highly motivated team. You will play a critical role in helping OpenAI ensure future models remain safe even as they grow in capability. This will make a significant impact on our goal of building and deploying safe AGI. The Interpretability team studies internal representations of deep learning models, aiming to understand model behavior and engineer models with more understandable representations, with a particular focus on ensuring the alignment of powerful AI systems.

$295k - $500k