2 days ago
Base Salary
$180k - $260k/yr
Responsibilities
- Set the multi-quarter technical strategy for data generation, curation, orchestration, distributed training, and related platform dependencies.
- Design and build difficult parts of the ML infrastructure stack while writing production code, prototyping approaches, and reviewing critical changes.
- Architect reproducible pipelines for discovering, labeling, versioning, and organizing fleet and simulation driving scenarios.
- Guide dataset quality, coverage analysis, label-quality analysis, and training and evaluation set construction.
- Lead distributed training, inference, evaluation, data loading, checkpointing, and experiment tracking on multi-tenant GPU clusters.
- Establish standards for model artifact management, experiment tracking, automated model testing, and deployment.
- Define and validate model and release metrics, experiments, baselines, ablations, and uncertainty estimates for safety-critical decisions.
- Lead, mentor, and grow engineers while breaking ambiguous problems into scoped work and guiding technical decisions.
- Represent the Scalable ML team in architecture and planning discussions and resolve cross-team infrastructure tradeoffs.
- Own system reliability and lead incident root-cause analysis, remediation, and prevention.
Requirements
- Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, Robotics, or a related field and 8+ years of relevant industry experience, or a master’s degree and 6+ years of experience.
- At least 2 years as a technical lead, Staff engineer, or similar role guiding other engineers.
- Expert Python software engineering skills and a record of writing and shipping high-performance production code as a leader.
- Deep experience building end-to-end ML systems covering data pipelines, dataset curation, training, evaluation, and production.
- Hands-on production experience with Ray, including running and debugging Ray workloads at scale.
- Deep experience with PyTorch and PyTorch Lightning for large-scale training, including DDP or FSDP, mixed precision, and checkpointing.
- Extensive experience designing and operating distributed systems and cloud infrastructure for ML, including GPU scheduling, quota, job orchestration, and multi-tenant clusters.
- Solid grounding in statistics and experimental design, including evaluation strategies, metrics, and thresholds for real-world decisions.
- Experience setting technical direction across teams and delivering complex cross-team initiatives from conception through production.
- Ability to influence and align peer teams and leaders without direct authority.
- Clear communication, mentoring, and collaboration skills across software, ML, and autonomy teams.
Benefits
- The posting emphasizes an inclusive and diverse work environment, collaboration, respect, agility, and opportunities to succeed and grow.
- The role is based with Gatik, headquartered in Santa Clara, California; no specific remote or hybrid arrangement is stated.
Categories
Data EngineeringML Engineering
About Gatik AI
Gatik AI builds and operates autonomous middle‑mile trucking networks for retailers and consumer brands, delivered as a transportation‑as‑a‑service offering. Its Level 4, Class 3–7 trucks move B2B freight on short‑haul, repeatable routes, with commercial deployments in Texas, Arkansas, and Ontario, including a fully driverless service launched with Walmart in 2021. Founded in 2017 and headquartered in Santa Clara, it integrates software and hardware to run driverless deliveries between warehouses and stores.