ML Infrastructure Engineer
Machines learned to understand language. We’re 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 novel sensors and edge hardware directly into live production environments, generating data that didn’t 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 world’s 10 largest food and beverage manufacturers and operates across hundreds of factories globally. Our systems have materially reduced waste, cut manufacturing downtime, saved water, chemicals, energy, and helped prevent safety and quality failures.
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.
The Role
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 Laminar’s 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 Laminar’s solutions and deepening our competitive moat by empowering researchers to deliver state-of-the-art technological advancements.
What You Will Do
- 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.
About You
- 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.
Benefits
- 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
- Work with real recognition: 2026 World Economic Forum Technology Pioneer, Gold 2026 Edison Award, Unilever Startup of the Year, Innovator Awards by both Coca-Cola and ABInBev, and more
- A team that celebrates together from rooftop lunches, ping pong matches, Lunch & Learns, and regular team events
Learn How We Think
- Learn about our startup journey: Our Journey
- How we're combating climate change: AI-Powered Climate Tech
- A customer story: Unilever uses Laminar precision automation to cut time & water usage
Our Interview Process
Skills
As published by lever · 5 questions · 2 written answers
Basics
Resume/CV, Full name, Email, Phone, Current location, Current company, LinkedIn URL, Twitter URL, GitHub URL, Portfolio URL
Pick from a list (3)
- Are you authorized to work lawfully in the United States for Laminar?
- Are you able to work in-person at least 3-4 days a week in Somerville, MA?
- Will you now or in the future require Laminar to commence (“sponsor”) an immigration case in order to employ you (for example, H-1B or other employment-based immigration case)? This is sometimes called “sponsorship” for an employment-based visa status.
Written answers (2)
- Would you like to share who referred you to this opportunity? If so, please include here. optional
- What interests you about this role, and how do your skills or experience make you a good fit?
