For a century, the answer to “what’s in the pipe right now” was a grab sample to a lab with results back hours later. We answer it every second.

Laminar is building human-centric self-driving factories: the ones made of stainless steel pipes and reactors, that make what the world eats, uses, and wears. Proprietary inline sensors read the chemistry in the pipe. Foundation models grounded in physics and chemistry reason about how close the process is to its target. Process control software acts on the answer through the plant’s automation system in real time. Laminar Insights shows what happened, why, and what it saved.

A Laminar line uses about 20% less water and 20% fewer chemicals and runs 15% faster, with quality protected. Productivity and sustainability improve together. We run across six continents at manufacturers like Coca-Cola, Unilever, and AB InBev, backed by tier-1 investors in Physical AI.

We’re a polymathic team of 50 in Somerville: hardware and software engineers, chemists, AI researchers, factory operators, and go-to-market wizards under one roof. Nobody here is waiting to be asked what to do. You’ll see the problem, propose the fix, and own the outcome. We believe in autonomy, ownership, and demanding excellence, and we hold each other to it. If that’s how you want to spend your time, come build the self-driving factory with us.

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.

Learn How We Think

Learn about our startup journey: Our Journey

How we're combating climate change: AI-Powered Climate Tech

Why You'll Love it Here

A front-row seat at Greentown Labs, North America's largest cleantech innovation hub, in a Somerville neighborhood ranked one of the coolest in the world
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
Competitive salary, equity, and a 401(k) with company match
Health, dental, and vision coverage, plus short- and long-term disability and life insurance
Flexible time off and 12 company-paid holidays
A conference and learning budget to keep growing your craft
Greentown Labs membership and a transportation benefit for your commute

Our Interview Process

1. Phone screen with Laminar Head of Ops or Recruiter (15-20 minutes)

2. Intro call with Hiring Manager (30 minutes)

3. On-site interview, including short tour of GTL, overview of tech, and interview/presentation with the Hiring Manager and a few team members. Depending on the role, a skills exercise that should take no longer than an hour to prep, would be sent ahead of time. We record your skills exercise to share with any team members who could not join the interview and/or with Founder's ahead of their Founder's Interview. If you are not local, we can conduct this virtually.

4. Finalists for Full-Time positions will have a Founder’s Interview in-person

Final steps:

Two professional references are requested, ideally one from your current organization and one who served as your Manager

If an Offer Letter is extended, a Background check is conducted

A recent study from LinkedIn showed that most women apply to jobs only when they meet 100% of the requirements, whereas men will hit the apply button if they hit 60%. Laminar (formerly H2Ok) is committed to building a diverse and inclusive team. So, to the women and nonbinary folks out there feeling unsure if you're a perfect fit, we strongly encourage you to apply! If you're ready to play a key role in scaling a game-changing company that’s transforming the industrial sector and advancing sustainability, we want to hear from you!

* We recently updated our domain to runlaminar.com. Please ensure you add the runlaminar.com domain to your safe senders list to ensure you receive our communications.

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