Engineering Manager - Data Intelligence

Applied Intuition•Sunnyvale, CA

7h•Onsite

About The Position

As an Engineering Manager on the Data Intelligence team, you'll lead a world-class group of engineers focused on revolutionizing how we produce, curate, and leverage high-quality data to accelerate autonomy development. Your team will work across three key areas: Data Quality, where we build systems to measure, validate, and improve the integrity of our datasets; Labeling Workflows, where we design intelligent annotation pipelines that combine human expertise with AI-powered automation; and Data Mining, where we surface rare, high-value driving scenarios from massive multi-modal fleet data. You'll drive the adoption of foundation models and cutting-edge AI techniques to scale these capabilities, setting technical direction and team goals that align with our model development, safety, and deployment milestones.

Requirements

3+ years of engineering management experience
Passion for building and leading high-performing teams
Experience building data quality systems, labeling pipelines, or annotation platforms at scale
Familiarity with modern ML infrastructure and data-centric AI approaches
Understanding of how foundation models can be applied to automate data workflows
Solid track record of building and deploying products

Nice To Haves

Direct experience with foundation models, including LLMs and VLMs, for data automation tasks
Background in autonomous driving or robotics perception
Experience with active learning, auto-labeling, or human-in-the-loop ML systems
Familiarity with 3D perception data (camera, lidar, radar)

Responsibilities

Grow and manage a team of world-class engineers with the goal of delivering high-quality, well-labeled data and identifying critical edge cases for autonomy
Prioritize development across data quality systems, intelligent labeling workflows, and large-scale data mining infrastructure
Lead the integration of foundation models (LLMs, VLMs, and multimodal models) to automate and enhance labeling, quality assurance, and data discovery
Evolve our data engine architecture to scale high-fidelity labels, reduce annotation costs, and accelerate ML iteration cycles
Set team goals and roadmap in alignment with training, evaluation, and deployment requirements
Partner with research, autonomy, and data infrastructure teams to ensure high-quality, relevant, and diverse data is powering our models
Drive hiring, mentoring, and growth for a high-performing, mission-driven team