Staff+ Software Engineer, Platform Intelligence

Platform Intelligence turns each new Claude model’s abilities into API features that developers can build on. Our goal is to make the full intelligence of each model available through the API. We own the capabilities that shape what Claude can do for developers: tool use, built-in tools such as web search and code execution, thinking controls, structured outputs, and the memory, files, and skills APIs.

We also run the API side of every Claude model launch, including its safety features and controls. That means getting the API ready for each new model, coordinating with research, safeguards, product, and cloud partners, and making sure the model, its features, the SDKs, and the docs all ship together on launch day.

The work sits where research meets product. We work with new models before they are public. When a model gains a new ability, we work out what it should look like as an API, build it, and ship it to developers on our own platform and on cloud platforms. Those decisions shape how developers first use each model. We also own API versioning and the evals that tell us a capability works in production.

What you’ll do

You’ll lead the design, build, and operation of API capabilities used by developers around the world. You’ll work with research to understand what a new model can do and turn that into an interface that is simple, durable, and hard to misuse.

You’ll lead the API side of new model launches. You’ll plan the work, track readiness across teams, resolve the issues that come up late, and see the launch through on the day. Between launches, you’ll make the next one faster and safer.

You’ll set technical direction for work that spans several teams, write clear design proposals, review API designs from other teams, and mentor engineers on the team.

Representative projects

• Lead API readiness for a Claude model launch, so that every feature works on day one across our API, SDKs, docs, and cloud partners
• Take a new model ability from research prototype to public API feature, from interface design to production rollout
• Build and operate tools such as web search and code execution under real traffic
• Automate the manual parts of a model launch
• Build the evals that catch a capability regression before a launch

You might be a good fit if you:

• Have led technical projects that spanned several teams, from design through launch and operation
• Have designed and operated production distributed systems where correctness, latency, and availability all matter
• Have strong product sense and care about API design and developer experience
• Make sound technical decisions with incomplete information, as models and priorities change close to a launch
• Explain tradeoffs clearly to research, product, and go-to-market partners, including non-engineers
• Make the engineers around you better through design review and mentoring
• Use AI tools as a regular part of how you design, build, and review software
• Care about the societal impacts of your work and Anthropic’s mission to build safe, beneficial AI

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

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.

• Working with large language models, including tool use, agents, or evals
• Running launches or migrations with fixed deadlines and many dependent teams
• Building tools for agents, such as search or code execution
• Storage, memory, or context systems at scale
• Integrating with cloud platforms such as AWS, Google Cloud, and Azure

Deadline to apply: None. Applications will be reviewed on a rolling basis.

Location Preference: Preference will be given to candidates based in NY, SEA, SF or the Bay Area given the current location of team.

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings (“OTE”) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $405,000-$485,000 USD, Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates’ AI Usage: Learn about our policy for using AI in our application process.

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., 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. And we value impact - advancing our long-term goals of steerable, trustworthy AI - rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We’re an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.