Tech Stack
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.
AI @ 2
- 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
Anthropic’s Human Data Interfaces team builds systems that collect data to improve AI models, including interfaces for data vendors, tooling, and front-end and back-end infrastructure that enables researchers to gather high-quality data at scale. The Software Engineer will own the architecture and execution of data collection pipelines, designing systems that are performant at scale and resilient to changing research needs. The role involves working with researchers, data operations partners, crowdworkers, and vendors.
Responsibilities
- Architect and build data collection pipelines that support rapid iteration while balancing data quality and system maintainability.
- Build clear and efficient interfaces for crowdworkers and vendors that lead to high-quality data.
- Collaborate with research teams to understand evolving data needs and iterate on collection methods.
- Partner with the Human Data Operations team to understand end-to-end data workflows and design interfaces that make their work easier.
- Prioritize and manage multiple workstreams, making trade-off decisions in a fast-moving environment with shifting research priorities.
Requirements
- Strong full-stack engineering experience across the technology stack.
- Experience building internal tools and working with their users to understand requirements.
- Ability to balance rapid iteration with long-term system health.
- Ability to quickly understand and work across complex technical systems.
- Bachelor’s degree or an equivalent combination of education, training, and/or experience.
- A field of study relevant to the role, demonstrated through coursework, training, or professional experience.
- Experience requirements correlate with the internal job level for the position.
Preferred Qualifications
- Experience building human data labeling interfaces, human-in-the-loop systems, or data collection pipelines.
- Familiarity with preference data and reward models used in AI model training.
- Experience working with researchers as internal users or customers.
- Experience building and improving the user experience of user-facing applications, particularly those involving complex UI interactions or annotation workflows.
- Strong system design instincts and the ability to build systems that evolve gracefully as requirements change.
- Experience influencing technical and product direction on a team.
Compensation
- Annual salary: $320,000–$405,000 USD.
Benefits
- Hybrid work policy, with staff expected to work from an office at least 25% of the time; some roles may require more office time.
- Visa sponsorship is available, although sponsorship cannot be successfully provided for every role and candidate.
- Competitive compensation and benefits.
- Optional equity donation matching.
- Generous vacation and parental leave.
- Flexible working hours.
- Office space for collaboration.
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