Machine Learning Infrastructure Engineer
- Silicon Valley, CA, United States
- $100k - $180k
- 0.05% - 0.25%
As a Machine Learning Infrastructure Engineer, you’ll help us build, scale and manage state-of-the-art machine learning infrastructure that powers the Matroid platform. You’ll own and operate systems that are responsible for dataset management, training, testing and deploying thousands of computer vision models at scale. You’ll also collaborate closely with the deep learning team to create practical computer vision applications.
You’ll be working onsite at our Palo Alto office, located near the Stanford campus and Caltrain.
- Build, scale and manage systems and infrastructure supporting the Matroid platform.
- Manage fleet of TensorFlow workers via Kubernetes.
- Explore new open-source systems for data management and search indexing.
- Develop prototypes and execute experiments to help guide engineering efforts.
- Bachelors in Computer Science or equivalent
- Experience with C++ or Python, Unix, databases, distributed computing, concurrent computing, databases and containerization (Docker).
- Strong experience in distributed systems, data management and operating systems.
- Industry experience with large-scale deployments of Kubernetes and TensorFlow across cloud platforms.
- Experience with statistical modeling across a diverse range of data sets and domains.
- Masters in Computer Science or equivalent.
What we offer in return
- Competitive pay and equity.
- The chance to constantly work on stimulating intellectual challenges.
- Gym membership reimbursement.
- Free, lunch, snacks, and caffeine every day.
- Medical, dental, and vision insurance with 100% coverage
- A flexible schedule that leaves time for all of your other interests.
- budget for whatever hardware will make you most effective.
- Budget and resources to learn about the cutting edge of software engineering and computer vision.
About Matroid, Inc.
With the rapid growth of artificial intelligence, more and more expert knowledge is required to use cutting edge AI techniques to solve real world problems.
At Matroid, we're building an intuitive product that allows anyone to train and deploy computer vision models without needing to know how to write a line of code. Founded by a Stanford professor in 2015, Matroid has raised $13.5 million in funding, and the product has been used in a variety of security and media applications.
The Matroid engineering culture emphasizes autonomy and rigorous quality. Matroid engineers have ownership of product areas, and are empowered to prioritize, design, and implement features in those areas. Engineers also have latitude in choosing the technologies best suited to meeting our product goals. We also encourage curiosity and learning about new technologies and parts of the stack you haven't worked on before.
Outside of work, we have frequent team outings, cookouts, and retreats, as well as weekly board games.
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