Jr.) Machine Learning; ML Developer

Our pay ranges are established per Pave Compensation Software. We’re also proud to offer equity in our fast-growing startup and one of the most comprehensive benefits packages among startups at our stage. Laminar pays 100% of the individual health insurance premium for HMO medical, vision, and dental, offers flexible PTO, a $90/month transportation benefit, a $65/month health and wellness benefit, FSA, 12 company-paid holidays, an employer-matching 401(k), and Greentown Labs membership, among other valuable resources.

At Laminar (formerly H2

Ok Innovations), we're leading the charge in cleantech innovation, reshaping process industrials and manufacturing to drive operational efficiency and sustainability for our world’s most foundational industries. Powered by our Laminar AI Co-pilot models and state-of-the-art sensors, our solutions optimize facility performance across various processes, including process manufacturing, production, water management, energy reduction, and waste minimization. Based at Greentown Labs, North America's premier cleantech innovation community, we're a woman-founded startup backed by renowned investors like Greycroft, Construct Capital, 2048 Ventures, and Flybridge Capital.

Our groundbreaking technologies have earned accolades and adoption from industry giants like Unilever, The Coca-Cola Company, ABinBev, and Mitsubishi Electric. We're committed to unlocking untapped data for our customers, empowering them to gain a competitive edge and create Industry 4.0.

Transforming our most foundational sectors of society is hard. Very hard. But we’re building an empire. And empire building is not easy. It’s deeply fulfilling, and you will learn and grow tremendously while driving sustainable impact globally with some of the largest players that make everything we eat, use, and wear. Our culture is to foster extraordinary growth within our teammates.

We believe in autonomy, ownership, empowerment, demanding excellence, being mission-driven. We believe in creativity, authenticity, and extraordinary growth. We’re looking for relentless, ambitious, creative, and exceptional people to join our team and build the factory of the future.

Build machine learning models that usher in the next generation of data-driven, fluid-based industrial processes powered by Laminar's proprietary spectral sensors and software platform.

Design and run experiments to evaluate and select machine learning models that are generalizable, accurate, and robust to day-to-day process variability.

Work with spectral and multi‑modal sensor data, building preprocessing and feature extraction pipelines that can derive insights from noisy, real‑world sensors.

Support model reliability by developing monitoring (and correction systems, when applicable) for model drift, sensor drift, and process anomalies.

Develop performant ML infrastructure and tooling in collaboration with ML/Data Scientists and software team members.

Work across problem domains including chemometrics, hybrid modeling, and self-supervised learning. Modeling tasks include distribution modeling, drift and anomaly detections, similarity analyses, and continuous calibration.

Required:

• Proficient in at least one Python ML framework (PyTorch, JAX, Tensor Flow).
• Fluent with Python packages for numeric computing and data workflows (Num Py, Polars, Pandas, scikit-learn).
• An engineer who favors clean, testable code and has a proven track record of delivering high-quality work on a timeline.
• An executor who thrives with direction and can independently complete technical project objectives.
• Someone detail-oriented who has a natural curiosity about data. You are enthusiastic to test hypotheses, understand in detail how our models work, and run physical experiments to improve our modeling capabilities.

Preferred:

• Chemical engineering, process engineering, or manufacturing domain knowledge (highly valued).
• Experience with cloud environments (AWS, GCP) and/or Databricks.
• Familiarity with spectral data, time‑series modeling, or sensor-driven ML.
• Familiarity with Bayesian modeling and probabilistic reasoning.
• Experience building real products (ideally…