ML Infrastructure Engineer
Confirmed on the employer's own hiring board on Sep 25, 2026. First seen by Alion on Sep 24, 2026. Laminar scores B on the Alion truth index.
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.
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.
Preferred(if any)
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