The Work
You will lead end-to-end delivery for software engineering AI training programs. The work includes coding datasets, repository-level tasks, agent workflows, reinforcement-learning environments, benchmarks, code review, and rubric-based evaluations.
You will turn research goals into practical workflows, improve quality and throughput, and keep programs on track across timelines, contributor performance, and cost.
- Own scope, timelines, quality, throughput, contributor performance, and cost.
- Design contributor workflows, review systems, capacity plans, quality controls, and reusable playbooks.
- Find bottlenecks and systematic dataset errors, then improve instructions, task order, incentives, and review structures.
- Build team-lead and reviewer systems for programs involving 100 to 1,000 or more contributors.
- Translate research goals into task specifications and challenge requirements that produce the intended signal.
- Use Python, SQL, or similar tools for quality sampling, defect analysis, throughput reporting, and operational reviews.
- Report progress, risks, quality trends, and recovery plans to AI research customers while mentoring other program leads.
What It Pays And Takes
This is a worldwide contract, part-time role. The listing does not specify pay. The role is marked entry level, and the work requires the technical and program skills listed below.
- Hours: 20 or more hours per week.
- Location: Open worldwide.
- Language: Fluent English.
- Work background: Experience leading complex, multi-stakeholder programs in software engineering, technical program management, consulting, finance, startups, operations, or a similar setting.
- Operations: Experience managing distributed teams, contributor networks, marketplaces, or large-scale technical operations.
- Analysis: Strong problem-solving skills, including finding bottlenecks, defining useful metrics, and improving production performance.
- Communication: Strong customer-facing communication skills, including managing expectations, communicating risks, and building lasting client relationships.
- Technical skills: Ability to read and review code, understand test suites, and independently assess technical work in Python, TypeScript, Java, or Go.
Helpful Experience
Experience with software engineering data programs, coding demonstrations, repository-level tasks, agent workflows, reinforcement-learning environments, benchmarks, code review, or rubric-based evaluation is useful. Familiarity with quality sampling, operational metrics, contributor management, and technical workflow design can help turn changing research requirements into dependable delivery systems.
How It Works
Apply on OpenTrain with your resume, then complete the application on the hiring site.
About AI Training Work
AI training is the human work behind systems that write and understand code. Contributors prepare examples, review model outputs, and evaluate technical work so AI systems can improve; software engineering specialists are needed when that work requires code and test-suite knowledge.