AI/ML Engineering Jobs

Anthropic seeks a senior Research Engineer with 7+ years of ML and computer vision expertise to advance Claude's visual and spatial reasoning capabilities. The role involves developing vision language model architectures, creating multimodal datasets and evaluations, and collaborating across teams to solve real-world customer challenges. This is a full-stack research position spanning pretraining, reinforcement learning, and deployment-time techniques.

Anthropic seeks a Research Engineer to build and optimize large-scale ML systems, focusing on safe and reliable AI infrastructure. The role spans cluster reliability, experiment design, code optimization, and tooling across their research platform. Ideal candidates combine strong software engineering expertise with machine learning knowledge and interest in AI safety.

Anthropic is seeking a Research Engineer to develop the next generation of large language models, working on model architecture, algorithms, and training infrastructure optimization. The role requires balancing cutting-edge research with practical engineering, including designing experiments, leading research projects, and contributing to the full ML stack from low-level optimizations to high-level model design. An advanced degree in CS/ML and expertise in Python/PyTorch with large-scale ML systems experience are essential.

Anthropic seeks Research Engineers/Scientists to develop next-generation multimodal large language models in their Zurich pre-training team. The role blends cutting-edge research with practical engineering, requiring expertise in model architecture, algorithm development, and large-scale ML systems optimization. Candidates should have strong software engineering skills, Python proficiency, and experience with deep learning frameworks, with flexibility across the researcher-engineer spectrum.

Anthropic seeks a Research Engineer/Scientist to advance audio capabilities in large language models, focusing on speech understanding, generation, and multimodal audio integration. The role requires 50/50 split between research and engineering work across the full audio ML stack, from signal processing to large-scale model training and deployment.

A hybrid research-engineering role at Anthropic focused on implementing and optimizing post-training techniques for frontier language models. The position requires strong software engineering skills combined with ML expertise to translate cutting-edge alignment research into production systems, with direct responsibility for model quality and safety.

Research Engineer focused on implementing and optimizing post-training techniques for Claude models at scale, including Constitutional AI and RLHF methodologies. The role requires strong software engineering skills, distributed systems expertise, and the ability to translate cutting-edge research into production-ready implementations while managing production incidents.

Research Engineer role at Anthropic focusing on pretraining and scaling of frontier language models. Requires hands-on experience training LLMs or deep expertise with JAX/PyTorch/TPUs and distributed systems, with a balanced 50/50 split between research and engineering work. Position involves ownership of production training pipelines, performance optimization, incident response, and cross-team collaboration in a high-impact, demanding environment.

This role combines research and engineering (50/50 split) to own critical aspects of Anthropic's production pretraining pipeline, including performance optimization, reliability, and debugging across the full ML stack. You'll design experiments to improve training efficiency, respond to production incidents during model launches, and build observability infrastructure for frontier-scale language models. The position requires hands-on experience with large-scale distributed training systems and a genuine passion for both research and engineering excellence.

Anthropic is hiring a Research Engineer for their Pretraining team to develop next-generation large language models at the intersection of research and engineering. The role involves conducting research on model architecture and algorithms, optimizing training infrastructure, and leading small research projects while contributing across the full stack from low-level optimizations to high-level design. Candidates should have an advanced degree in ML/CS, strong Python and PyTorch expertise, and experience with large-scale language model training.

Research Engineer role on Anthropic's Code RL team focused on improving Claude's ability to write correct, optimized code for accelerators like GPUs. You'll design RL environments and evaluation metrics, conduct experiments, and deliver work into production training runs, requiring deep expertise in both reinforcement learning and accelerator performance optimization.

Anthropic seeks a Research Engineer to advance reinforcement learning capabilities for large language models, including autonomous systems, code generation, and agentic tool use. This hybrid research-engineering role involves architecting scalable RL infrastructure, designing novel training environments and evaluations, and implementing fundamental research at production scale. The position requires both deep technical expertise in RL and strong systems engineering skills to push the boundaries of AI capabilities.

Anthropic seeks a Research Engineer to advance reinforcement learning capabilities in large language models, focusing on agentic systems, tool use, and code generation. The role blends research innovation with engineering excellence, requiring design and implementation of novel RL training environments, infrastructure optimization, and collaboration across research and production teams to scale cutting-edge systems.

Anthropic's Interpretability team seeks a Research Engineer to reverse-engineer how language models work at a mechanistic level, building tools and infrastructure for AI safety. The role combines neuroscience-inspired analysis of neural networks with large-scale distributed training challenges, requiring both research depth and engineering rigor to scale interpretability across production systems.

This role focuses on building and evaluating autonomous AI systems to understand and defend against adversarial use of advanced AI. You'll develop model organisms of self-improving systems and create defensive agents, directly influencing Anthropic's safety research and AI policy at a critical juncture in AI development.

This role focuses on building and scaling RL training environments for LLM adaptation across new domains and use cases. The ideal candidate combines ML research expertise in fine-tuning and reward design with strong project management and vendor relationship skills to deliver end-to-end capability improvements to Anthropic's models.

Research Engineer role focused on building scalable data infrastructure and pipelines that process Claude usage logs while maintaining privacy for Anthropic's economic research team. The position requires expertise in data engineering, system design, and infrastructure development to support high-impact AI economic research across the organization.

Anthropic seeks a Senior Research Engineer to build large-scale infrastructure for training and deploying AI scientist systems capable of advancing scientific frontiers. The role requires 6+ years of infrastructure engineering expertise with deep knowledge of distributed systems, performance optimization, containerization, and ML training pipelines. Success demands end-to-end ownership of infrastructure blockers, evaluation frameworks, and deployment architectures supporting long-horizon AI tasks and reinforcement learning at scale.

Anthropic is seeking a Research Engineer to advance AI safety in cybersecurity by developing reinforcement learning approaches for secure coding and vulnerability remediation. The role requires blending research innovation with strong engineering implementation, combining domain expertise in cybersecurity with experience in AI training and safety.

Design and build AI-powered observability systems that analyze massive volumes of unstructured data to provide insights for AI safety, misuse detection, and alignment monitoring. Work across the full stack from core analysis frameworks to user-facing applications, partnering with researchers and safety teams to build high-leverage tools used across the organization.