Software Engineer, Research Productivity

Anthropic; New York (NY); Hybrid; USD 405,000 - 625,000; Full time

Job summary

Anthropic's Research Productivity group builds infrastructure to accelerate research. We seek an experienced software engineer to scale this infrastructure, partnering with researchers and delivering multi-month projects from scope to production. You will shape architecture, set standards, and mentor teammates as workloads grow.

Candidates should have a track record with large-scale distributed systems, strong engineering fundamentals, and clear communication across teams. Hybrid policy applies.

Qualifications

• Experience designing, building, and operating large-scale distributed systems or infrastructure in production.
• 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.

Responsibilities

• Design, build, and scale infrastructure and systems that support rapidly increasing research usage.
• Independently scope and lead complex, multi-month engineering projects from start to production.
• Drive cross-organizational alignment on technical direction with multiple stakeholders.
• Make architectural decisions that shape the foundation of research infrastructure and tooling.
• Partner with researchers to understand workflows and design for evolving needs.
• Iterate quickly with pragmatic, first-principles solutions and fast feedback loops.
• Take ownership of reliability and scalability of critical systems as load grows.
• Help set technical standards and mentor other engineers.

Skills

Distributed systems

Project scoping

Software fundamentals

System architecture

Communication

Adaptability

Tools

ML infra

Cloud infra / IaC

Scalability

Job description

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 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

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. This team operates like a startup within a startup. You'll be a strong fit if you like thinking from first principles, iterating fast on infrastructure, and tackling reliability and scalability challenges head-on as the products built on top of your systems evolve underneath you. You'll independently scope complex, multi-month projects, drive cross-org alignment through ambiguous problem spaces, and make the architectural decisions that shape how the infrastructure behind our research tooling gets built. You'll partner directly with research teams to understand their workflows, anticipate how their requirements will change, and design and scale infrastructure that can keep pace.

• 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

Minimum Qualifications

• 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

Strong candidates may also have

• Experience building infrastructure or platforms specifically for research or machine learning workflows
• Direct experience navigating the reliability and architectural challenges that come with rapidly scaling systems
• Experience with distributed systems, cloud infrastructure, and infrastructure-as-code
• Familiarity with the compute, tooling, and workflow needs of large-scale machine learning research
• Experience operating in a startup or startup-like environment, i.e. a small, fast-moving team with high autonomy
• Prior experience as a technical lead or mentor for other engineers

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

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.

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.

Guidance on Candidates' AI Usage:

Learn about our policy for using AI in our application process.