About the Role
Instawork is on a mission to create meaningful economic opportunities for skilled hourly professionals in communities around the globe. Our AI-powered labor marketplace helps local businesses scale, and enables global technology companies to push the frontiers of robotics and AI. Instawork’s on-demand labor marketplace is uniquely valuable for robotics and physical AI training. We’re working with leading frontier labs to create the highest-quality, highest-diversity dataset for training robotics foundation models. Instawork Robotics is the human advantage in the robotics revolution.
The Instawork Robotics ML Lead will own the technology strategy for physical AI training data.
Who You Are
- An mid-career developer, with at least 8 years of programming and/or engineering leadership experience, excluding internships.
- A machine learning expert, with a Master’s or PhD in an AI or ML-related field and at least 2 years of professional experience building ML or CV models for robotics or autonomous vehicle applications.
- Experienced with data pipelines, with expertise in distributed systems, cloud computing on AWS infrastructure, and scalable data processing for large-scale datasets.
- An innovator, with a background in identifying and translating techniques from academic research and frontier labs into production systems.
- A problem-solver, who enjoys building new-to-world systems, sharing your technical expertise with other builders, and applying creativity and grit to create value for our robotics partners.
Nice-to-haves
- 5+ years experience designing or architecting production distributed systems
- Experience shipping production code at an early-stage or mid-stage startup
- Comfort and presence leading technical discussions with external partners and customers
What You’ll Do
- Model Development - plan, architect, and build the data labeling and enrichment pipeline at the foundation of Instawork Robotics’ data offering.
- Pipeline Optimization - identify and implement continuous improvements to our data pipeline to improve efficiency, scalability, and quality.
- Dataset Quality - develop methodologies and analytics to measure the quality and performance of our dataset and ML models.
- Research Reviews - stay on top of academic research and industry best practices related to robotics learning and training.
- Cross-Functional Collaboration - Work closely with robotics leadership, data ops, QA, and other engineering teams to ensure alignment from concept to deployment.
- Mentorship & Influence - Guide mid and senior-level engineers through code reviews, pair programming, and architectural discussions, raising the technical bar across the organization.