Senior ML Engineer

Job Description: Senior ML Engineer Location: Ann Arbor, Michigan (Remote) Role Overview This position is for a Senior Machine Learning Engineer to join a team focused on developing end-to-end Vision-Language-Action (VLA) models for autonomous vehicles. The team is responsible for converting petabytes of multi-modal fleet data into high-quality, semantically rich training datasets. This role will own the dataset layer that these advanced models learn from, playing a part in the data flywheel that includes mining failure cases, auto-labeling at scale, and validating data quality. Key Responsibilities - Design, implement, test, and deploy cloud-based pipelines to convert logged multi-sensor data into VLM/VLA training datasets. - Develop open-vocabulary detection, dense captioning, and semantic enrichment using foundation models to scale annotation. - Produce language-grounded reasoning and chain-of-causation style annotations to support VLA training. - Surface rare, difficult, and high-uncertainty scenarios to build curated datasets that improve downstream model metrics. - Define dataset schemas, quality metrics, and validation processes, tracking auto-labeling quality against model requirements. - Partner with VLM/VLA model developers to define dataset specifications and operationalize a continuous delivery loop. - Build distributed, reproducible pipelines using columnar data formats and distributed compute. - Serve as a project lead, mentor engineers, conduct design reviews, and set coding and annotation standards. - Track advances in multimodal models, auto-labeling, and end-to-end autonomous driving to apply relevant research. Required Qualifications Education: A Bachelor's Degree in Computer Science, Robotics, Electrical Engineering, or a related technical field with competences typically acquired through 6+ years of experience, or a Master's Degree in a related technical field with competences typically acquired through 3+ years of experience. Experience: Must be highly skilled and proficient, able to conduct complex work under minimal supervision. Expected to drive alignment across team interfaces, own technical solutions, and mentor other engineers. Technical Skills: A combination of the following skills is required: - Computer Vision & Deep Learning: Model training and expertise in at least two of the following: 2D/3D Object Detection, Tracking, Sensor Fusion, Semantic Segmentation, BEV, or Depth Estimation. - Multimodal / VLM Experience: Hands-on work with vision-language models, open-vocabulary recognition, dense captioning, or semantic search applied to perception data. - Model Data Curation: Building targeted datasets and large-scale Parquet data processing (e.g., Databricks, Daft, Pandas). - Distributed ML & Data Frameworks: Experience with PyTorch, Lightning, Ray, Spark, or equivalent. - Scaled MLOps & Tooling: Knowledge of experiment tracking, model registry (e.g., MLflow, Weights & Biases), and ML metrics. - Development Tools: Strong Python software development skills, experience with VDI and cloud-based environments, CI systems (e.g., GitHub Actions), and Docker. Preferred Qualifications Familiarity with VLM/VLA or end-to-end driving models and reasoning-trace datasets. Experience with auto-labeling foundation models for annotation and verification. Knowledge of high-throughput model serving frameworks like vLLM or SGLang. Experience with semantic inference, retrieval, and vector databases such as LanceDB. Familiarity with AV data standards (e.g., Pegasus layers) and robotics formats