Software Engineer, Research Infrastructure

About the role

Anthropic's Research Productivity organization builds the infrastructure and systems that accelerate research across Anthropic, including the tools that help our research process get faster and more effective over time. We're looking for an experienced software engineer to join us as we scale this infrastructure at a moment when both demand and scope are growing extremely quickly.

Responsibilities

• Design, build, and scale infrastructure and systems that support rapidly increasing usage, where requirements and workload continue to evolve as the products built on top of them evolve
• Independently scope and lead complex, multi-month engineering projects, from an ambiguous starting point through to a production system
• Drive cross-organizational alignment on technical direction, working through ambiguous problem spaces with multiple stakeholders and teams
• Make architectural decisions that shape the foundation of research infrastructure and tooling across Anthropic
• Partner directly with researchers to deeply understand their workflows, then anticipate and design for how those needs will change
• Iterate quickly, favoring pragmatic, first-principles solutions and fast feedback loops over heavy upfront design
• Take ownership of the reliability and scalability of critical systems as load, usage, and complexity increase
• Help set technical standards and best practices for the team, and mentor other engineers

Requirements

• Experience designing, building, and operating large-scale distributed systems or infrastructure in production
• A track record of independently scoping and delivering complex, ambiguous, multi-month technical projects
• Strong software engineering fundamentals and hands-on coding ability
• Experience making architectural decisions that other engineers and teams build on top of
• Strong written and verbal communication skills, with experience driving alignment across multiple teams or stakeholders
• Demonstrated ability to operate effectively in ambiguous, fast-changing environments

Qualifications

• 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

Pay

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—$625,000 USD

Logistics

• 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.

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, 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.