Strategic project lead - Software Engineering | Remote
Company Description:
Curvia AI is a London, UK-based technology and AI solutions company that transforms complexity into opportunity through enterprise solutions, AI-powered automation, and data-driven insights. We build practical, scalable, and human-centered technology grounded in integrity, security, and responsible innovation.
Role Overview
We are looking for a Strategic Project Lead with direct experience operating software-engineering data programs for frontier AI models.
You will own production across coding data, RLHF or preference data, SWE benchmarks, model evaluations, agentic coding, code generation, and code repair. This includes contributor calibration, throughput, quality, rework, cost, timelines, and delivery recovery.
The role requires strong technical judgment. You should be able to read code and tests, inspect final artifacts, identify invalid work, and make release or rejection decisions with minimal training.
Offer Details:
- Commitments Required: At least 8 hours per day and minimum 40 hours per week with overlap of 4 hours with PST.
- Employment type : Contractor assignment (no medical/paid leave)
- Contract length: Short Term Contract (3 month)
- Mode of Work: Fully Remote
- Number of positions: 10
- Pay: US $125-$150 per day (net/take-home)
Skill
We need candidates who have already operated in software-engineering data programs for frontier models. This is not a general AI TPM, enterprise AI delivery or data-platform role. The person should be able to take over an active SWE data project with minimal training.Prioritize people from Mercor, Scale AI, Surge AI, Handshake, xAI or comparable companies who have personally owned one or more of the following:
- Coding training or post-training data
- Coding RLHF or preference data
- SWE benchmarks or model evaluations
- Agentic coding environments
- Code-generation or code-repair datasets
- The candidate must have owned the production system, including SWE contributor sourcing and calibration, task throughput, quality, rework, cost and delivery recovery.
- They should be able to read code and tests, inspect the final artifact, diagnose why an invalid task passed QA, and make a release or rejection decision
What Does Day-to-Day Look Like?
- Operate active software-engineering data and evaluation programs end to end.
- Manage contributor sourcing, calibration, onboarding, reviewer capacity, and performance.
- Track throughput, quality, rework, defects, cost, and delivery timelines.
- Review code, tests, task metadata, and final artifacts for technical correctness.
- Diagnose quality failures and improve task design, rubrics, instructions, and review processes.
- Lead recovery plans when programs fall behind on quality, throughput, cost, or timelines.
- Coordinate distributed software engineers, reviewers, and team leads.
- Communicate progress, risks, and corrective actions to customer research and program teams.
- Use Python, SQL, or similar tools for quality analysis, defect tracking, and operational reporting.
Requirements
- Direct experience operating production software-engineering data programs for frontier AI models, preferably involving coding training, coding RLHF, SWE benchmarks, model evaluations, agentic coding, code generation, or code repair.
- Proven ownership of the full production system, including contributor sourcing and calibration, task throughput, quality, rework, cost management, and delivery recovery.
- Strong software-engineering judgment, with the ability to read code and tests, inspect final artifacts, identify invalid technical work, and make release or rejection decisions.
- Experience managing distributed software-engineering contributors, reviewers, and team leads while maintaining consistent quality and delivery performance.
- Ability to take over an active program quickly, diagnose operational problems independently, and communicate clear decisions and recovery plans to demanding customers.
Evaluation Process :
AI Interview (~20 mins)
Delivery interview (~45 mins)