Instrumental is creating the future of manufacturing, empowering hardware companies to optimize their factories through the use of artificial intelligence. Thanks to Instrumental, companies can:
- increase yields (the percent of goods passing inspection);
- decrease dark yield (the escape of goods that should have failed inspection); and,
- trace everything (to narrowly scope recalls to only those goods affected).
Our product is delighting customers, who now have access to technology that produces results previously unattainable. The Instrumental customer list is growing across a diverse set of manufacturing applications, and we are asking you to help us scale to the needs of that entire market.
With ~30 people we are a small but mighty team, so if you are looking for a place where you can work with friendly people and have an outsize impact on entire industries then keep reading!
About the role:
As a Full Stack Engineer in Machine Learning Infrastructure at Instrumental, you'll apply your expertise to implement infrastructure and product features necessary for our Machine Learning efforts.
This includes infrastructure and tools for performing ML research, backend infrastructure to run ML models at scale, and product changes that use these models.
Our stack: Python for ML research and production, Scala for backend code, Typescript for frontend and to drive hardware on the factory side. We use Docker for managing the execution environment and AWS as the cloud provider. We don’t expect prior experience with all the technologies involved, but you will be expected to learn to use them effectively.
This isn't a solo job -- you will be working together with both ML engineers and engineers owning all the components of our stack, operations, and product staff in a quest to improve the way that things are made, wherever they are made.
What you can expect in the first few months:
- Designing and implementing tools and infrastructure for ML research and scalable deployment
- Partnering with other engineers on the ML team to deliver key defect detection features onto the hardware assembly lines of the world.
- Using instrumentation and automated testing to maintain and improve software performance.
- Three or more years of full-stack exposure in modern applications. This means work experience with a modern frontend (HTML/CSS/JS or mobile apps) and a modern backend (cloud and distributed systems).
- Demonstrated ability to lead teams in speccing & delivering software projects while keeping stakeholders in the loop.
- Ability to effectively communicate complex ideas to technical and non-technical individuals alike.
- Not required: Machine Learning background
- Not required: prior experience with Scala or Typescript. Just the desire to learn!
Python, HTML, CSS, JS/TypeScript, Docker, SQL, Machine Learning