Senior ML Engineer

Role Description

Own the offline dataset pipeline:

• Design, implement, test, and deploy Cloud-based pipelines that convert logged multi-sensor data into VLM/VLA training datasets.
• Spanning geometric labels (3D/2D detection, tracking, segmentation, depth) through semantic, scenario-level, and action/trajectory-grounded annotations.

Build VLM-assisted auto-labeling:

• Develop open-vocabulary detection, dense captioning, semantic enrichment, and scene/scenario description generation.
• Move beyond closed-set bounding boxes, using foundation models to scale annotation and cut manual labeling cost.

Generate reasoning-grounded labels:

• Produce language-grounded reasoning and chain-of-causation style annotations.
• Temporally aligned to ego-motion and trajectories to support VLA training and explainable driving behavior.

Mine and curate the long tail:

• Surface rare, difficult, and high-uncertainty scenarios.
• Build curated datasets that measurably improve downstream VLM/VLA model metrics.

Close the data flywheel:

• Define dataset schemas, quality metrics, and validation.
• Track auto-labeling quality against model requirements.
• Route model failures back into re-labeling and retraining loops.

Partner with the end-to-end model team:

• Co-define dataset specifications with VLM/VLA model developers.
• Own the quality bar and delivery cadence.
• Operationalize a continuous dataset delivery loop into their training pipelines.

Scale on cloud infrastructure:

• Build distributed, reproducible pipelines using columnar data formats and distributed compute.
• Maintain disciplined software practices, version control, and documentation.

Lead and mentor:

• Serve as project lead, guide less-experienced engineers.
• Run design reviews, set coding and annotation standards.
• Drive alignment across team interfaces to the rest of the organization.

Stay current:

• Track the latest advances in multimodal models, auto-labeling, and end-to-end autonomous driving.
• Translate relevant research into production data systems.

Qualifications

• Bachelor’s Degree in Computer Science, Robotics, Electrical Engineering, or related technical field plus competences typically acquired through 6+ years of experience; OR Master’s Degree in a related technical field plus competences typically acquired through 3+ years of experience.

Requirements

• Computer Vision & Deep Learning: model training and at least two of: 2D/3D Object Detection, Tracking, Sensor Fusion, Semantic Segmentation, BEV, Depth Estimation.
• Multimodal / VLM experience: hands-on work with vision-language models, open-vocabulary or zero-shot recognition, dense captioning, or semantic embeddings/search applied to perception data.
• Model Data Curation: building targeted datasets that measurably improve downstream model performance; large-scale Parquet data processing (Databricks, Daft, Pandas, etc.).
• Distributed ML & data frameworks: PyTorch, Lightning, Ray, Spark, or equivalent for training and large-scale data processing.
• Scaled MLOps & Tooling: experiment tracking, model registry, MLflow / Weights & Biases, and ML metrics, evaluation, and quality.
• Development Tools & Eco-System (at scale): strong Python software development, VDI and cloud-based development environments, CI systems (GitHub Actions), and Docker.

Benefits

• A competitive compensation package that includes a bonus component and stock options.
• 100% paid medical, dental, and vision premiums for full-time employees.
• 401K plan with a 6% employer match.
• Flexibility in schedule and generous paid vacation (available immediately after start date).
• Company-wide holiday office closures.
• AD+D and Life Insurance.