Strategic Project Lead, Software Engineering
$120k - $200k • Remote • New York, New York, United States; San Francisco, California, United States; Seattle, Washington, United States
Posted 23d ago
Job Location
New York, New York, United States; San Francisco, California, United States; Seattle, Washington, United States
Tech Stack
Remote Work Policy
Fully remote
Categories
AI Engineer
About the job
Turing is seeking a Strategic Project Lead for their software engineering data programs. In this role, you will be responsible for the production system that generates high-quality datasets, reinforcement learning environments, and research benchmarks for frontier AI labs. You will translate complex research requirements into predictable delivery, managing aspects like quality, throughput, contributor performance, timelines, and cost. This is an operations leadership position with a technical component, requiring you to understand code, tests, and quality signals to optimize workflows and rubrics. Your primary goal is to build and operate a system that consistently produces high-quality technical work at scale.
Responsibilities
- Design and manage data pipelines from customer specification to final delivery, ensuring accountability for scope, timeline, and quality.
- Diagnose and resolve bottlenecks in real-time by re-sequencing workflows, refining instructions, creating incentive systems, and scaling review processes.
- Conduct daily syncs to proactively address issues before they impact customers.
- Serve as the primary point of contact for researchers and program managers at AI labs, managing expectations and reporting.
- Build long-term trust with clients and identify opportunities for project expansion.
- Source, vet, onboard, train, and manage performance of distributed domain experts.
- Maintain high execution standards throughout the production lifecycle, including annotation, review, and delivery.
- Design motivation and performance systems, including gamification, to keep large contributor pools engaged and productive.
- Own quality control across the annotation lifecycle, setting standards, measuring performance, and addressing deviations.
- Analyze datasets to identify trends, anomalies, and systematic errors, and implement root cause fixes.
- Implement and continuously improve best practices for annotation, evaluation, and curation.
- Stay informed about emerging practices in AI data operations and apply them proactively.
- Champion workflow changes to reduce task completion times and improve cost efficiency.
- Maintain clear, scalable documentation for process improvements.
- Codify operational processes, including onboarding scripts, quality benchmarks, contributor management frameworks, and escalation patterns.
- Document your domain's section of the knowledge base and mentor future hires.
Requirements
- Background in consulting, finance, startups, or other operationally intense environments.
- Proven track record of managing complex, multi-stakeholder projects.
- Strong analytical and communication abilities, including identifying bottlenecks, building measurement plans, and communicating solutions to clients.
- Customer-facing experience, including managing expectations and building long-term relationships with high-profile clients.
- Ability to inspect code, understand tests, and interrogate quality signals.
- Willingness to challenge workflows or rubrics that are not producing intended results.