ML Engineer - Instawork Robotics

Ovii's Interpretation of the Role

We seek an ML Engineer to build and scale data pipelines that power robotics and autonomous‑vehicle training. The role blends machine‑learning model development, computer‑vision expertise, and large‑scale distributed systems on AWS. You’ll translate cutting‑edge research into production‑ready pipelines for our robotics partners.

Role Snapshot

• ML Engineer
• Robotics data pipelines
• Computer‑vision models
• AWS cloud infrastructure
• Distributed systems
• Production‑grade code

Must-Have Requirements

• Machine learning model development
• Computer vision model development
• Distributed systems expertise
• AWS cloud computing
• Scalable data processing for large datasets
• Data pipeline design and implementation
• software engineering
• ML/CV model building
• Master’s or PhD in AI or ML‑related field

Nice-to-Have Signals

• Design/architect production distributed systems (3+ years)
• Shipping production code at early‑stage or mid‑stage startup
• Technical discussions with external partners/customers
• Translating academic research techniques into production systems
• designing production distributed systems
• startup code shipping
• technical discussions with partners

Work Setup

• Location: San Francisco, United States
• Work mode: ONSITE
• Employment type: Full-Time

Not Specified in JD

• Remote eligibility
• Visa sponsorship
• Travel requirement

What You'll Likely Work On

• Design, build, and maintain the data labeling and enrichment pipeline for robotics training data.
• Continuously improve pipeline efficiency, scalability, and quality.
• Create metrics and analytics to assess dataset and model performance.
• Stay current with academic research and industry best practices in robotics learning.
• Collaborate with robotics leadership, data ops, QA, and engineering teams to align concepts through deployment.

Good Fit If You Have

• 3+ years designing or architecting production distributed systems (nice‑to‑have).
• Experience shipping production code at an early‑stage or mid‑stage startup (nice‑to‑have).
• Participating in technical discussions with external partners or customers (nice‑to‑have).