[Hiring] Senior ML Engineer @ITCO Solutions

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 rather than simply adding volume.
• 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.
  • Employ 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, and 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

• End-to-end / VLA driving:
  • Familiarity with VLM/VLA or end-to-end driving models, trajectory and action grounding, or chain-of-causation/reasoning-trace datasets.
• Auto-labeling foundation models:
  • Experience with segmentation, open-vocabulary detectors, or VLM/LLM-driven data engines for annotation and verification.
• High-throughput model serving:
  • vLLM, SGLang, or similar for batch auto-labeling and inference at scale.
• Semantic inference & retrieval:
  • Attribute mapping, semantic search, and vector databases (e.g., LanceDB) for automotive data.
• AV data standards & tooling:
  • Scenario-description standards such as Pegasus layers; parsing robotics formats (ROS bags, MCAP) and optimizing columnar storage (Parquet, Arrow).
• Cloud development & orchestration:
  • Terraform and AWS managed services (S3, ECS, Lambda, DynamoDB, Step Functions, Athena); AWS HyperPod / Anyscale; inference orchestration.
• Data visualization:
  • Foxglove, FiftyOne (51), three.js, OpenGL, or similar for dataset inspection and accessibility.
• Evaluation & research:
  • Closed-loop/open-loop evaluation frameworks (e.g., NavSim-style planning metrics); publications in top-tier CV/AI/Robotics venues (CVPR/ECCV/ICCV, NeurIPS/ICLR/ICML, CoRL).