Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
CI/CD @ 4
Distributed Systems @ 8
Communication @ 7
AI @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
About Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. The company builds AI systems with focus on safety and societal benefit and comprises researchers, engineers, policy experts, and business leaders working together on large-scale research efforts.
Role description
Join Developer Acceleration within the Developer Productivity organization to help build infrastructure that enables thousands of employees to be productive via agents. The team owns agent runtime and internal knowledge systems, and you will shape how AI transforms internal engineering workflows by building agent runtimes, tooling, and context-management systems.
Responsibilities
- Define technical strategy and roadmap for your area and translate goals into execution
- Build and ship runtime platforms and tooling for agent provisioning and execution
- Build agent harnesses and experiment with context management strategies to improve agent performance and correctness
- Write evals to benchmark agent behaviors and measure quality
- Own infrastructure scalability, reliability, and operational excellence practices
- Collaborate cross-team to deliver impact and support internal partners
Requirements
- 10+ years building and operating large-scale distributed systems
- 3+ years leading large-scale, complex projects or teams as an engineer or tech lead
- Significant experience with agents (agents performing technical/knowledge work)
- Experience building scalable platforms and owning platform reliability and operational practices
- Strong communication skills and experience working with internal partners (researchers and engineers)
- Interest in developer productivity, transforming how teams work, and building productivity tooling
Strong candidates may also have
- Deep understanding of how human work will evolve alongside AI-driven tools
- Experience with container or VM orchestration at scale
- Developer productivity or infrastructure experience (CI/CD, builds, etc.)
- Experience building widely adopted CLI tools and services
- Experience working closely with researchers and engineering teams
Compensation
- Annual salary: $405,000 - $485,000 USD
Logistics
- Minimum education: Bachelor’s degree or equivalent combination of education, training, and/or experience
- Location-based hybrid policy: staff expected to be in an office at least ~25% of the time (some roles may require more)
- Visa sponsorship: Anthropic states they do sponsor visas and retain an immigration lawyer to assist, though sponsorship is not guaranteed for every role/candidate
- Applications are reviewed on a rolling basis (no stated deadline)
How we’re different
Anthropic emphasizes large-scale, high-impact AI research done as a cohesive team. The organisation values collaboration, communication, and pursuing long-term goals in AI safety and steerability.