Machine Learning Engineer, Instrumental
$162-180k
Office located in Palo Alto, CA
Vision-powered AI to detect manufacturing anomalies
Job no longer available
Vision-powered AI to detect manufacturing anomalies
21-100 employees
Job no longer available
$162-180k
Office located in Palo Alto, CA
21-100 employees
Company mission
To empower manufacturing teams to solve problems faster with the right data, at the right resolution, in front of the right people, in real time.
Role
Who you are
- We’re seeking a highly customer-centric ML Engineer who will join our cross-functional engineering team
- Proven experience delivering ML features from research to production with a preference for candidates with an exposure to application development
- At least 2 years of delivering production systems in Python, Java, or Scala
- Start-up or equivalent experience where you demonstrate strong attention to detail and ownership balanced with a scrappy, get-stuff-done, mentality
- 2+ years of machine learning research and development experience; professional use of deep learning or computer vision is a bonus!
- You have a computer science or related background with solid fundamentals in algorithms and the math behind them
- Feel at home communicating research and other complex ideas to a broad swath of the company including engineers and non-engineers
- This role requires authorization to access information that is subject to U.S. export control restrictions
What the job involves
- You’ll be responsible for working with our talented engineers to build and maintain a highly scalable end-to-end ML production pipeline. The role requires a mix of research and productization responsibilities, both delivered in a rapid, iterative clip
- Build and own machine learning pipelines end-to-end! For us, this means:
- Maintain an obsessive focus on delivering value to our customers
- Maintain ownership of ML pipelines, all the way to surfacing the features to customers, and measuring their impact
- Partner closely with the entire R&D organization, working in a highly collaborative environment where you’ll be exposed to the entire scope of a deliverable rather than just the ML portion of the project
- Quickly prototype and iterate on new algorithmic concepts, and prioritize them based on customer needs
- Develop and deploy large-scale ML systems using the relevant state-of-the-art AI approaches to solve the problem
- Guide efforts to acquire high-quality datasets
- Manage and improve the ML pipeline, from data management, model management, and resource scheduling
Our take
Inefficiencies in manufacturing are widespread and costly, approximately to the tune of $8 trillion. Instrumental is a company improving the speed and quality of electronics hardware manufacturing through software identifying defects, cutting waste, work, development times, and improving communication and collaboration across organizations.
It opted for a “continuous scale” style contract with manufacturers, allowing the possibility to charge more based on volume, rather than renegotiating during annual renewal periods. Moreover, electronics is a huge global market, and Instrumental have been careful to offer solutions that work with high manufacture but low internet-connectivity facilities, like many in China.
The company closed a round of series C funding, which will be used to expand research development for new products. With brands including Axon, Bose, Cisco, and SolarEdge using Instrumental's cloud platform and AI already, it is likely this list will expand following investment.
Freddie
Company Specialist
Insights
Company
Funding (last 2 of 4 rounds)
Feb 2022
$50m
SERIES C
Jul 2020
$20m
SERIES B
Total funding: $80.3m
Company benefits
- Work from home opportunities
Company HQ
Palo Alto, CA
Articles
Founders
Studied Mechanical Engineering at Stanford. Was a Mechanical Engineering Intern at D2M. Worked at Apple for over 5 years, promoted from iPod Product Design Engineer to Apple Watch System Product Design Lead & Manager.
Samuel Weiss
(CTO)Studied Mechanical Engineering at Stanford, and MIT. Was a Design Consultant at Cues Inc. Interned in iPod Product Design at Apple. Was a Mechanical Engineering Intern for Joby Energy. Was a Product Design Engineer for Apple Watch.
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