ML Infrastructure Engineer

OaklandFull Timemid

DevOps & InfrastructureInfrastructure EngineerInfrastructure & Cloud

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Requirements Summary

be able to hire, manage,

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DevOps & InfrastructureInfrastructure EngineerInfrastructure & Cloud

BlueSpace.ai is revolutionizing the autonomous vehicle industry with our groundbreaking self-driving technology. Unlike conventional autonomy software, our patented 4D Predictive Perception removes reliance on data. By leveraging next-gen 4D sensors, we can precisely predict the motion of all objects, increasing accuracy, lowering latency, and setting a new standard for safety and efficiency in autonomy.

Our team consists of seasoned professionals from across the autonomous vehicle (AV) ecosystem, including OEMs, world-class research institutions, and leading autonomous driving companies. With a proven track record of successful AV service launches in California, Texas, and Florida, we are well-positioned to drive the next generation of autonomy.

We are seeking talented, self-motivated individuals who thrive in a fast-paced, dynamic environment and are passionate about making a significant impact on the future of mobility. If you’re driven to tackle the most complex challenges in autonomy and push the boundaries of what is possible, we want to hear from you.

As the ML Infrastructure Engineer at Bluespace, your mission will be to develop the Cloud Data Infrastructure System to support and enable our Assured AI for Autonomy.

You will have the opportunity to design and develop the infrastructure, which will allow our developers to expand Bluespace’s APNT capabilities.

Take ownership of the infrastructure in support of developing and deploying Machine Learning models for Autonomous Vehicles

Architect and deploy cloud and on-prem ML training and evaluation infrastructure

Own the the data management pipelines, from ingestion and storage, to model training and evaluation that span vehicle compute, cloud, and on-prem

Change model training code to take advantage of the better data storage techniques and formats you propose

Evaluate and implement methods, software, and hardware for model deployment onto the test and production vehicles

Develop systems and processes to improve transition of models from research to production while balancing cost

Participate in model design, research and set requirements to model design that ensure their successful deployment

Own and deliver projects end-to-end

Optional: be able to hire, manage, or at least mentor other engineers who join this project when growth is needed

Experience in architecting and implementing data engineering solutions for a small engineering team / product (1-20 ppl)

2+ years of software engineering experience in any of the following: ML Infrastructure, Data Engineering, Platform Engineering, Distributed Systems

Either existing experience with ML Infrastructure as described below, or strong expertise in non-ML Data infrastructure combined with a strong desire to learn ML Infra specifics

Production ML experience with at least one of the following - (1). Model conversion and optimization for production (ONNX, TensorRT), (2) Model deployment on specialized hardware (e.g. Jetson), or (3) Model monitoring and MLOps

Ability to programmatically access cloud services using Python, NodeJS, or equivalent

Knowledge of or experience with data management solutions, such as - (1) Workflow orchestration pipelines (e.g. Argo, Airflow, Kubernetes) or (2) Managed large-scale data processing systems (e.g. Spark, Dataproc, Databricks)

End-to-end ML pipelines (e.g. SageMaker, Vertex)

Experience with Terraform or a similar IAC solution

Robotics background, C++, ROS

Experience with Google Cloud Platform or other major cloud provider