Staff+ Software Engineer, Enterprise
Anthropic · San Francisco, CA | New York City, NY
- Location
- San Francisco, CA | New York City, NY
- Salary
- $405,000 - $485,000 USD
- Funding
- $15.3B
- Posted
- Jul 1, 2026
Anthropic is hiring a Staff+ Software Engineer, Enterprise based in San Francisco, CA | New York City, NY. Every apply link on Engg.space goes straight to the company's own careers page - no recruiter middleman, no generic job-board form.
Apply directly at AnthropicRole details
About Anthropic 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. About the role Enterprise is central to Anthropic's mission. The organizations that could benefit most from Claude often have the most demanding needs, with rigorous requirements around end-user experience, procurement, security, and permissions. We believe earning their trust is essential to ensuring AI benefits the world broadly. We're looking for a Staff+ Engineer to anchor our Enterprise pillar—the team that makes Claude enterprise-ready at scale. When a Fortune 500 company wants to roll out Claude to 100,000 employees, the systems and products that you design are what make it possible. This is a high-agency role that spans multiple efforts within Enterprise. You'll design Claude Enterprise to be widely distributed, support our governance and connectivity story, design abstractions that other teams want to adopt, help the team evaluate tradeoffs, and be the engineer in the room who can interface with executives from the world’s largest companies. Responsibilities Define and drive multi-year technical strategy, including our distributability and multi-cloud strategy Identify and personally lead the highest-complexity, highest-impact engineering initiatives spanning multiple teams. Serve as one of the primary technical decision-makers for major architectural decisions with org-wide scope. Partner other teams such as Inference and Platform for foundational systems; Customer Success for the voice of customers, with Infrastructure and Safeguards for security, privacy, and responsible deployment. Mentor and develop Staff-level engineers across the org. Drive alignment across Product, GTM, Customer Success, and beyond while proactively identifying and addressing systemic technical risks. You may be a good fit if you: Have 10+ years of engineering experience with a clear track record operating at Staff or Senior Staff level. Have demonstrably shaped technical strategy for large-scale Cloud Service Provider or Enterprise SaaS companies Drive the highest-leverage technical outcomes without formal authority—you lead through influence, quality of thinking, and trust. Have deep expertise in distributed systems and API architecture, and are effective writing design docs, making architectural calls, and coding in critical paths. Are highly effective across org boundaries—you build trust with GTM, Platform, Inference, Infrastructure, Privacy, Safeguards, and business stakeholders alike. Bring strong domain expertise and intuition in enterprise areas such as security, identity, compliance, governance, permissions, and connectivity. Technical Stack Languages: Python, Golang, Rust Infrastructure: GCP, AWS, Azure, Kubernetes Databases: PostgreSQL (AlloyDB), Vector Stores, Firestore, Spanner Tools: Feature Flagging, Prometheus, Grafana, Datadog, Claude Code 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 Logistics 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. 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. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different 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, a
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