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# ML Engineer

- **Company:** Everforth Apex
- **Location:** Ann Arbor, MI, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** LanceDB, Artificial Intelligence, Amazon Web Services, Computer Vision, Cloud Computing, Cloud Engineering, Continuous Delivery, Data Validation, Data Visualization, Programming Tools, Github, Python (Programming Language), Machine Learning, Language Modeling, Object Detection, Sensor Fusion, Parquet, Data Processing, Pytorch, Apache Spark, Deep Learning, Pandas, Information Technology, SGLang, Integration Frameworks, Machine Learning Operations, Terraform, Docker, Databricks
- **Published:** October 5, 2026
- **Apply:** https://www.dice.com/job-detail/c973c0c6-f870-48a3-a871-53c0ddabfae8

## About the Role

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 (e.g., ROS bags, MCAP).
 * Proficiency in cloud development and orchestration, including Terraform and AWS managed services.
 * Experience with data visualization tools like Foxglove or FiftyOne.
 * Contributions to closed-loop/open-loop evaluation frameworks or publications in top-tier AI/Robotics venues.

## Description

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., * 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.