ML Infrastructure Engineer

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. 

• 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.