Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
At Anthropic, we believe the most impactful safety research will require access to frontier AI systems. The most powerful AIs will operate not just on text but also other modes of data, including images, video audio. Such models have potential to augment human creativity and productivity in exciting ways. However, we are very concerned about the risks introduced by powerful multimodal AIs. The Multimodal team at Anthropic builds and studies multimodal models to better understand and mitigate these risks.
Our team works across many parts of a large stack that includes training, inference, system design and data collection. Some of our core focus areas are:
Foundational Research
We develop new architectures for modeling multimodal data and study how they interact with text‑only models at scale.
Building Infrastructure
We work on many infrastructure projects including:
* Complex multimodal reinforcement learning environments.
* High‑performance RPC servers for processing image inputs.
* Sandboxing infrastructure for securely collecting data.
Data Ingestion
We are more interested in running simple experiments at large scale than smaller complex experiments. This requires access to very large sources of multimodal data. We develop tooling to collect, process and clean multimodal data at scale.
Because we focus on so many areas, the team is looking to work with both experienced engineers and strong researchers, and encourage anyone along the researcher/engineer curve to apply.
You may be a good fit if you:
* Have significant software engineering experience
* Are results‑oriented, with a bias towards flexibility and impact
* Pick up slack, even if it goes outside your job description
* Enjoy pair programming (we love to pair!)
* Want to learn more about machine learning research
* Care about the societal impacts of your work
Strong candidates may also have experience with:
* High performance, large‑scale ML systems
* GPUs, Kubernetes, PyTorch, or OS internals
* Language modeling with transformers
The expected base compensation for this position is below. Our total compensation package for full‑time employees includes equity, benefits, and may include incentive compensation.
Logistics
Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
Location‑based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren’t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.
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