Technical Lead, Scalable ML Infrastructure

Gatik; Santa Clara (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Gatik is seeking a Hands-on technical leader to set direction for Scalable ML and collaborate with autonomy, simulation, safety and data teams. You will own architecture for fleet and simulation data, scaling model training and evaluations, and drive rigorous release decisions backed by metrics.

You will prototype, write production-grade code, mentor engineers, and partner with platform teams to align on GPU scheduling, job orchestration, storage, and compute costs.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Robotics, or related field; 8+ years of experience, or a Master’s with 6+ years and 2+ years in a lead role.
• Expert Python software engineering; production-quality, high-performance code as a leader.

Responsibilities

• Define and own the multi-quarter technical strategy for data generation, curation, orchestration, and distributed training.
• Design and build core stack components; set code quality, testing, and review standards.
• Architect scalable data pipelines with versioning, provenance, and data contracts.
• Guide dataset quality; ensure balanced training/evaluation sets across routes and conditions.
• Lead design of distributed training, inference, and evaluation pipelines on multi-tenant GPU clusters.
• Drive model artifact management, experiment tracking, and automated testing/deployment.

Skills

Python engineering

Distributed systems

ML systems

PyTorch/PyTorch Lightning

Ray (Ray Core/Data/Train/Tune)

GPU scheduling & multi-tenant clusters

Leadership & mentoring

Education

Bachelor's degree in CS/EE/related

Master's degree (advantage)

Tools

SQL

Pandas

Git

Job description

Gatik is seeking a Hands-on technical leader to set direction for Scalable ML and collaborate with autonomy, simulation, safety and data teams. You will own architecture for fleet and simulation data, scaling model training and evaluations, and drive rigorous release decisions backed by metrics.

You will prototype, write production-grade code, mentor engineers, and partner with platform teams to align on GPU scheduling, job orchestration, storage, and compute costs.