Design, build, and maintain automated CI/CD/CT pipelines for Machine Learning models. Design the deployment workflow to validate model safety before physical deployment.
Optimize Machine learning models by building inference engines. You will manage GPU resource allocation on embedded devices to balance latency with power constraints.
Provision and manage scalable cloud infrastructure to support distributed training clusters and simulation environments.
Design and maintain a multi-modal data pipeline capable of ingesting and synchronizing massive streams of LiDAR, Camera, and IMU data. You will build the storage and retrieval layers that fuel our training sets.
Implement robust monitoring to track model performance and data drift. You will be responsible for building telemetry to monitor system health and inference latency on the edge to ensure robot safety.
Implement Feature Stores and strict Data Versioning to ensure total reproducibility of models. You will ensure we can trace every robot decision back to the exact code, data, and model version used.
Maintain and evolve AIM’s ML data platform, including large-scale multi-modal data pipelines (LiDAR/Camera/IMU), schema/metadata standards, dataset indexing, and high-throughput storage + retrieval layers to support training, evaluation, and simulation workflows.

3+ years of professional experience in MLOps, DevOps, or Software Engineering, with a focus on productionizing ML models.
Proficiency in Python and C++
Proven experience optimizing Deep Learning models with CUDA ONNX and TensorRT
Experience building large-scale ML data lakes and dataset pipelines (e.g., Ray Datasets / Ray Data) for ingestion, transformation, versioning, and retrieval of multi-modal training data.
Strong command of Docker and Kubernetes
Hands-on experience with cloud architecture.
Mastery of CI/CD tools
Familiarity with handling non-structured robotics data formats (e.g. Rosbags, MCAP, PCD files)
Experience with high-throughput data tools like Apache Kafka, Airflow, or Spark
Ideal candidate will have additional experience in several of the following areas:
Experience working with ROS / ROS2
Experience deploying to embedded compute platforms
Experience integrating ML pipelines with simulators

Receive company funded medical, dental, vision, 401k, life insurance, gourmet food & perks
Experience a strong onsite collaboration (AIM offices, labs and proving grounds on the east side of the Greater Seattle area)
Have an opportunity to travel to unique sites around the world (Americas, Australia, Africa & more)

MLOps Engineer

Key skills