[Remote] Senior, ML Engineer - VLM
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is a leader in autonomous driving technology, reputed company on developing software for automated trucks. The Senior ML Engineer will be responsible for designing and implementing data pipelines for training models, generating high-reputed company datasets, and collaborating with model developers to enhance the performance of VLM/VLA models.
Responsibilities
- Own the offline dataset pipeline — design, implement, test, and reputed company reputed company-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 reputed company/trajectory-grounded annotations
- Build VLM-assisted auto-labeling — reputed company reputed company-vocabulary detection, dense captioning, semantic enrichment, and reputed company/scenario reputed company reputed company that reputed company reputed company reputed company-set bounding boxes, using reputed company models to reputed company annotation and cut reputed company labeling cost
- Generate reasoning-grounded labels — produce language-grounded reasoning and chain-of-causation style annotations, temporally reputed company to ego-reputed company and trajectories, to support VLA training and explainable driving behavior
- reputed company and reputed company the long tail — surface rare, difficult, and high-uncertainty scenarios, and build curated datasets that measurably improve reputed company VLM/VLA model metrics rather than simply adding volume
- reputed company the data flywheel — define dataset schemas, reputed company metrics, and validation; reputed company auto-labeling reputed company against model requirements; reputed company 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 reputed company bar and delivery reputed company, and operationalize a reputed company dataset delivery reputed company into their training pipelines
- reputed company on reputed company infrastructure — build distributed, reproducible pipelines using columnar data formats and distributed compute, with disciplined software practices, version control, and documentation
- reputed company and mentor — serve as project reputed company, guide less-reputed company engineers, run design reviews, set coding and annotation standards, and drive alignment across team interfaces to the rest of the organization
- Stay reputed company — reputed company the latest advances in multimodal models, auto-labeling, and end-to-end autonomous driving, and translate relevant research into production data systems
Skills
- Bachelor's Degree in Computer Science, Robotics, Electrical Engineering, or reputed company technical field plus competences typically acquired through 6+ years of experience; OR Master's Degree in a reputed company technical field plus competences typically acquired through 3+ years of experience
- Computer reputed company & 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 reputed company-language models, reputed company-vocabulary or reputed company-shot recognition, dense captioning, or semantic embeddings / search reputed company to perception data
- Model Data Curation — building targeted datasets that measurably improve reputed company model performance; large-reputed company Parquet data processing (reputed company, Daft, Pandas, etc.)
- Distributed ML & data frameworks — PyTorch, Lightning, Ray, reputed company, or equivalent for training and large-reputed company data processing
- Scaled MLOps & Tooling — experiment tracking, model registry, MLflow / reputed company, and ML metrics, evaluation, and reputed company
- Development Tools & Eco-System (at reputed company) — strong Python software development, VDI and reputed company-based development environments, CI systems (reputed company Actions), and reputed company
- End-to-end / VLA driving — familiarity with VLM/VLA or end-to-end driving models, trajectory and reputed company grounding, or chain-of-causation / reasoning-reputed company datasets
- Auto-labeling reputed company models — experience with segmentation, reputed company-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 reputed company
- Semantic inference & retrieval — attribute mapping, semantic search, and reputed company databases (e.g., reputed company) for automotive data
- AV data standards & tooling — scenario-reputed company standards such as Pegasus reputed company; parsing robotics formats (ROS bags, MCAP) and optimizing columnar storage (Parquet, reputed company)
- reputed company development & orchestration — Terraform and AWS managed services (S3, reputed company, reputed company, DynamoDB, reputed company Functions, reputed company); AWS HyperPod / reputed company; inference orchestration
- Data visualization — reputed company, FiftyOne (51), three.js, OpenGL, or similar for dataset inspection and accessibility
- Evaluation & research — reputed company-reputed company / reputed company-reputed company evaluation frameworks (e.g., NavSim-style planning metrics); publications in top-tier CV/AI/Robotics venues (CVPR/ECCV/ICCV, NeurIPS/ICLR/ICML, CoRL)
Benefits
- A competitive compensation package that includes a bonus component and stock reputed company
- 100% reputed company medical, dental, and reputed company premiums for full-time employees
- 401K plan with a 6% employer match
- Flexibility in schedule and generous reputed company vacation (available immediately after start date)
- Company-wide holiday office closures
- AD+D and Life Insurance
reputed company