The Role

As our company grows and scales, we are excited for a ML Developer to join the team!We are looking for ambitious, hard-working recent graduates who want to be at the forefront of bringing AI to fluid & process manufacturing As a ML Developer, you will own the development and refinement of Laminar's machine learning models - the heart of our process optimization technology. Your work will affect all of Laminar's key process optimization models across domains including (but not limited to): CIP (clean-in-place), product changeovers, material identification, and emerging use-cases.

You'll work closely with ML/Data Scientists to bring cutting-edge models all the way from prototype to production. This entails scaling up model training methodologies, crafting experiments, and running ablation studies across a wide and diverse range of domains, all with the goals of increasing model accuracy and reliability. Your work will be instrumental to hyper-scaling Laminar's solutions and unlocking key markets through enabling new use-cases.

What You Will Do

• 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

About You

• Proficient in at least one Python ML framework (PyTorch, JAX, TensorFlow)
• Fluent with Python packages for numeric computing and data workflows (e.g. NumPy, 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 out 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 utilizing machine learning) 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
• 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

$89,000 - $141,000 a year

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) (unheard of at this stage!), and Greentown Labs membership, among other valuable resources. (*subject to change)

There are many easier places to work on AI. Laminar is for people who want the hardest version of their discipline. Models here must survive contact with physics. Hardware must survive years of continuous industrial operation. Software must integrate with machinery built decades ago. Everything we build ultimately has to work on a factory floor.

We believe exceptional people should be given exceptional amounts of ownership. At Laminar, you will have the context to form your own view, the permission to challenge ours, and the resources to pursue the right answers, whatever technical or organizational boundaries stand in the way. There are few layers between identifying something important and changing it.

We're fortunate to work with a small polymathic team of hardware and software engineers, chemists, AI researchers, factory operators, and go-to-market wizards who are unusually capable, curious, rigorous, ambitious, and low-ego. If that sounds like you, we'd love to meet you.

Our Interview Process

1. Phone screen with Laminar HR/Recruiter (15-20 minutes)

2. Intro call with Hiring Manager (30 minutes)

3. On-site interview, 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