Strategic Project Lead - STEM

Turing•San Francisco, CA

15h•Remote

About The Position

We are looking for a seasoned Delivery Manager (8+ years of experience) to lead large-scale data annotation and curation projects in support of cutting-edge LLM and multimodal AI research. You will oversee end-to-end delivery for projects with 100+ team members, ensure world-class data quality, and drive process and tooling innovation in collaboration with AI researchers and cross-functional teams. This role is ideal for someone who combines deep technical expertise, proven leadership in managing large teams, and a passion for advancing AI/ML innovation. Given that our key customers operate in the data analytics, data engineering, and Business Intelligence (BI) space, the ideal candidate will bring hands-on leadership experience with platforms such as Databricks and Snowflake, enabling them to deeply understand customer domains, speak their language, and deliver annotation outcomes that are contextually relevant and technically sound.

Requirements

10+ years of experience in engineering, program management, or delivery of large-scale projects.
Proven ability to lead and scale large teams (100+ members) with measurable impact.
Strong technical grounding in AI/ML, data annotation, and multimodal systems.
Hands-on leadership experience in data analytics, data engineering, or BI domains, with working knowledge of platforms such as Databricks and/or Snowflake (architecture, pipelines, data modeling, and production workflows).
Familiarity with the modern data stack including ETL/ELT pipelines, data warehouses, data lakes, and BI tools (Power BI, Tableau, Looker, or similar).
Ability to translate complex data engineering and analytics concepts into annotation frameworks, quality standards, and training materials.
Proficiency in Python (preferred) or Swift for data analysis and automation.
Strong mathematical and analytical skills for data-driven problem solving.
Experience in data curation, annotation pipelines, and quality control.
Excellent communication and stakeholder management skills.
Track record of driving high-quality outputs under tight deadlines.

Nice To Haves

Advanced degree in Computer Science, Data Science, AI, or related fields.
Experience applying gamification to workforce engagement.
Familiarity with modern project management tools & methodologies.
Exposure to state-of-the-art multimodal AI technologies.
Prior experience managing annotation or data labeling projects for customers in the data engineering, analytics, or BI space.
Familiarity with SQL at an advanced level and data orchestration tools such as Apache Airflow, dbt, or Prefect.
Certifications in Databricks (e.g., Data Engineer Associate) or Snowflake (e.g., SnowPro Core) are a plus.

Responsibilities

Lead, mentor, and grow large-scale teams of annotators and trainers.
Drive performance management, career development, and targeted training.
Foster a culture of excellence, ownership, and continuous learning.
Design and implement motivation strategies (including gamification) to boost engagement and reward top performers.
Define and manage project scope, objectives, timelines, and quality metrics.
Align with cross-functional stakeholders on goals and deliverables.
Proactively identify and resolve delivery risks, blockers, and dependencies.
Provide clear and consistent reporting on progress, challenges, and solutions.
Own quality control and data integrity across the annotation lifecycle.
Analyze datasets to identify trends, anomalies, and insights for improvement.
Implement best practices for annotation, evaluation, and data curation.
Share learnings and continuously improve quality processes.
Partner with engineering and research teams to optimize internal tooling.
Stay ahead of emerging trends in AI, ML, and multimodal processing.
Champion innovations that reduce task completion times and improve efficiency.
Maintain clear and scalable documentation for processes and training.
Serve as a domain-knowledgeable point of contact for customers operating in data engineering, analytics, and BI environments.
Leverage hands-on experience with platforms like Databricks and Snowflake to understand customer data workflows, terminology, and quality expectations.
Translate customer-specific data engineering and BI contexts into clear, actionable annotation guidelines and quality benchmarks for delivery teams.
Collaborate with customers to define annotation taxonomies and evaluation criteria that align with their data pipeline and analytics use cases.