ML Infrastructure Engineer

Lever, Inc.; Somerville (MA); On-site; USD 89,000 - 141,000; Full time

Benefits offered by this job

Competitive salary

Equity

401k

Health benefits

Greentown Labs network

Paid time off

Job summary

Laminar is seeking a ML Infrastructure Engineer to own the development of infrastructure and tooling that enables researchers to train, evaluate, and deploy models at scale. You will ensure seamless integration with edge devices and factory-floor deployments across domains.

You will interface with ML researchers and data engineers to build frictionless pipelines, improve model deployment, and scale Laminar’s ML capabilities.

Qualifications

• Proficient with cloud platforms to train and evaluate ML models at scale.
• Experience using experiment tracking and model lifecycle tools (MLflow, wandb).
• Strong Python and SDK skills (boto3, databricks-sdk, mlflow).
• Familiar with SQL, Databricks/Spark, and parquet data formats.
• Engineer who builds easy-to-use tools for researchers.
• Detail-oriented with large-scale ML training and edge deployment challenges.

Responsibilities

• Develop orchestration tooling for researchers to launch modeling jobs (training, fine-tuning, inference).
• Design auto-evaluative test environments using semi-supervised metrics and priors.
• Build model registries and automated deployment pipelines for edge devices.
• Create monitoring tools to detect drift and trigger continuous training pipelines.
• Collaborate with ML researchers and engineers to meet needs and integrate with existing infra.
• Build custom solutions tailored to Laminar's unique use-cases.

Skills

AWS & Databricks

MLflow & wandb

Python & SDKs

SQL & Spark

Research tooling

Edge deployment

Tools

Databricks SDK

boto3

mlflow

Job description

Machines learned to understand language. We’re teaching them to understand matter.

Forty percent of global manufacturing happens through physical and chemical processes inside pipes, tanks, and reactors. Despite decades of industrial automation, much of what happens inside them remains remarkably invisible. Manufacturing is the most ubiquitous and foundational sector in global economy, yet the way factories are fundamentally run have used the same control philosophies, manual operations, and legacy software for the past 60 years.

Laminar deploys novel sensors and edge hardware directly into live production environments, generating data that didn’t previously exist to build foundation models deployed in factory floors that understand chemistry, composition, quality, and material state in real time. We use that understanding to run autonomy and rethink how things are made.

The last generation of industrial automation taught machines to execute instructions reliably. The next will teach them to understand the processes they control and run autonomously, adaptively, and agentically: higher quality, safety, more efficiently, sustainably, and productively.

That future is already taking shape. Today, Laminar works with 7 of the world’s 10 largest food and beverage manufacturers and operates across hundreds of factories globally. Our systems have materially reduced waste, cut manufacturing downtime, saved water, chemicals, energy, and helped prevent safety and quality failures.

We are backed by tier-one investors in physical AI to make intelligent, self‑improving production the new standard for industry.

Join us to build what makes matter intelligible, and the intelligible controllable.

The Role

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.
• Transportation benefit for your commute
• Work with 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

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 Head of Ops or 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

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 is committed to building a diverse and inclusive team.

* 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.