ML Infrastructure Engineer
Job Location:
Somerville, NJ - USA
Yearly Salary:
Posted:
Application Deadline:
Vacancies:
Job Summary
Machines learned to understand language. Were teaching them to understand matter.
Forty percent of global manufacturing happens through physical and chemical processes inside pipes tanks and reactors. Despite decades of industrial automation much of what happens inside them remains remarkably invisible. Manufacturing is the most ubiquitous and foundational sector in global economy yet the way factories are fundamentally run have used the same control philosophies manual operations and legacy software for the past 60 years.
Laminar deploys state-of-the-art patented sensors and edge hardware directly into live production environments generating data that didnt previously exist to build foundation models deployed in factory floors that understand chemistry composition quality and material state in real time. We use that understanding to run autonomy and rethink how things are made.
The last generation of industrial automation taught machines to execute instructions reliably. The next will teach them to understand the processes they control and run autonomously adaptively and agentically: higher quality safety more efficiently sustainably and productively.
That future is already taking shape. Today Laminar works with 7 of the worlds 10 largest food and beverage manufacturers and operates across hundreds of factories globally across six continents. Our systems have materially reduced waste cut manufacturing downtime saved water chemicals energy and helped prevent safety and quality failures. Our technology has gained international recognition from being selected as a 2026 World Economic Forum Technology Pioneer Gold 2026 Edison Award Unilever Startup of the Year to Innovator Awards by both Coca-Cola and AB InBev and more.
We are backed by tier-one investors in physical AI to make intelligent self-improving production the new standard for industry.
Join us to build what makes matter intelligible and the intelligible controllable.
As our company grows and scales we are excited for a ML Infrastructure Engineer to join the team! We are looking for a thoughtful and hard-working infrastructure engineer who wants to play an integral role in bringing AI to fluid & process manufacturing. As a ML Infrastructure Engineer you will own the development of infrastructure and tooling that helps ML researchers train evaluate and deploy models at scale. Your work will directly power the vertical and horizontal scalability of Laminars ML models across domains including (bot not limited to): CIP (clean-in-place) product changeovers material identification product filtration and emerging use-cases.
You will interface with ML researchers and data engineers to build infrastructure that allows researchers to frictionlessly train models on large-scale data evaluate them on unseen data and deploy champion models to run on the factory floor across edge devices. Your tooling will be fundamental to making our research-to-production ML pipeline faster and more hands-free ensuring a seamless experience for researchers. Your work will be instrumental to hyper-scaling Laminars solutions and deepening our competitive moat by empowering researchers to deliver state-of-the-art technological advancements.
- Develop computer orchestration tooling for researchers to seamlessly launch modeling jobs on large-scale data training fine-tuning inference.
- Design model testing environments that automatically evaluate model performance without a human in the loop through semi-supervised metrics and process-aware priors.
- Build model registries and automated deployment pipelines that support large-scale model tracking versioning and deployment on edge devices.
- Develop monitoring tools for deployed models: detect model drift or anomalies then trigger continuous training (CT) pipelines as needed.
- Work with ML researchers ML developers to design systems that meet their needs; work with software engineers to design systems that interact gracefully with existing infrastructure.
- Build for our unique use-cases and problems not for the average problem.
- Highly experienced using cloud platforms (AWS Databricks) to train and evaluate ML models on large-scale data.
- Experienced using off-the-shelf tools (MLflow wandb) for experiment tracking and model lifecycle management (versioning artifact registry deployment monitoring).
- Highly experienced with Python and relevant SDKs (boto3 databricks-sdk mlflow); familiar with modern ML frameworks (jax pytorch).
- Familiar accessing data through SQL Databricks/Apache Spark and raw parquet formats.
- An engineer who thrives on building easy-to-use tools that researchers love to use.
- Highly detail-oriented: you understand the nuances in our workflows and respect the challenges that come with large-scale ML training and deployment to edge devices.
- Open-minded and independent thinker well-versed in building tailor-made solutions that address real pain points.
- An executor who can both independently complete technical project objectives and provide domain expertise to guide engineering design decisions.
Preferred (if any)
- Chemical engineering process engineering or manufacturing domain knowledge (highly valued).
- Past experience working with spectral data time-series data or sensor data.
- Experience building or evaluating custom ML models.
- Experience building real products and practicing user-centric design.
- Direct impact on product and culture.
- Comprehensive benefits package including Medical Dental Vision Life Insurance Disability Transportation benefit Health and Wellness benefit and more.
- 401k plan with employer matching
- Equity
- Competitive salary and bonus opportunities.
- Dynamic and inclusive work environment.
- Opportunities for growth and professional development.
- Access to Greentown Labs extensive network of cleantech startups.
- Transportation benefit for your commute
- A team that celebrates together from rooftop lunches ping pong matches Lunch & Learns and regular team events
- Learn about our startup journey:Our Journey
- How were combating climate change:AI-Powered Climate Tech
- A customer story: Unilever uses Laminar precision automation to cut time & water usage
Our Interview Process
Required Experience:
IC
About Company
There are many easier places to work on AI. Laminar is for people who want the hardest version of their discipline. Models here must survive contact with physics. Hardware must survive years of continuous industrial operation. Software must integrate with machinery built decades ago. ... View more
