Jobs, auto-discovered from public company career pages; Aspirova links out to the original source.
Open opportunities
22170 opportunities
Anthropic
Anthropic's Safeguards team is responsible for enforcing our policies, protecting users, and ensuring our platform is not misused.
We're seeking an exceptional Research Scientist to join our Life Sciences team at Anthropic. Our team is building a world-class research group focused on making Claude a superhuman life sciences research assistant.
To learn more about the skills we look for and how to prepare for this role, see our blog post - So You Want to Work in Mechanistic Interpretability?
We're looking for a research engineer who believes that visual and spatial reasoning are core to fully unlocking the capabilities of LLMs.
The Universes team within Research is responsible for training AI models to perform complex, difficult, long-horizon agentic tasks in ultra-realistic settings.
Our blog provides an overview of topics that the Alignment Science team is either currently exploring or has previously explored.
This role lives at the boundary between research and engineering. The problems are open, the experiments run at frontier scale, and the path from a robust result to production is short.
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts.
You'll work at the intersection of cutting-edge research and production engineering, implementing, scaling, and improving post-training techniques like Constitutional AI, RLHF, and other alignment methodologies.
This role lives at the boundary between research and engineering. You'll work across our entire production training stack: performance optimization, hardware debugging, experimental design, and launch coordination.
Anthropic is at the forefront of AI research, dedicated to developing safe, ethical, and powerful artificial intelligence.
You'll need to know accelerator performance well to turn it into tasks and signals models can learn from.
We're looking for Research Engineers to build the evaluations that tell us - and the world - what Claude can actually do.
The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs.
As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models.