Machina Labs
Machine Learning Engineer
Chatsworth, CA · On-site · Unspecified
- Discipline
- Software
- Industry
- Manufacturing
Spec sheet
Automatically summarized from the employer’s posting. Check the original description below for full requirements.
Engineering
Two sentences, spec-sheet tone. Place, craft, constraints. No slogans. Do not start with "Two sentences".
Required
- machine learning
- data pipelines
- robotic sheet metal forming
- production-scale ML
- data wrangling
- model deployment
- manufacturing analytics
Preferred
- experience with manufacturing data
- robotics
- industrial AI
- automation systems
- cloud ML platforms
Employer description
About Machina Labs
Engineering moves at software speed. Manufacturing doesn’t. Yet.
Machina Labs is changing that. We build intelligent, software-defined factories that produce complex metal structures directly from digital design. By integrating advanced metal forming, robotics, and automated production inside a flexible factory architecture, we enable customers to move from prototype to production in weeks, not years.
Backed by Lockheed Martin, Toyota, and NVIDIA, we’re building the manufacturing infrastructure that defense, aerospace, and advanced mobility programs will run on.
If you want to work on hard problems that matter and see them fly, drive, and defend, this is the place.
Job Description:
We are looking for a Machine Learning Engineer to join our team and help us push the boundaries of what’s possible in smart manufacturing. In this role, you will design, build, train, and deploy machine learning models that power our robotic sheet metal forming systems. You’ll work closely with our engineering team to transform raw data into actionable intelligence, enabling our robots to produce parts with greater precision, speed, and adaptability.
This is a hands-on role for someone who thrives at the intersection of research and production, someone who is just as comfortable wrangling messy datasets as they are architecting scalable ML pipelines. If you’re passionate about applying machine learning to real-world manufacturing challenges, we’d love to hear from you.
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