Senior Machine Learning Operations Engineer

We're a small team doing big things — using cutting-edge physics and real-time sensing to change how the world sprays crops.

Why Ag Zen

Our technology is in fields today, helping farmers spray smarter and use fewer chemicals. The work you do here has a measurable effect on sustainability and profitability across millions of acres.

Born out of MIT research, AgZen sits at the intersection of hardware, software, and agronomic science. If you thrive on tackling genuinely novel challenges and shipping quickly, you'll fit right in.

You won't be a cog in a machine here. Every person on the team has direct influence over the product and the company's direction — and we want to keep it that way as we grow.

We're Hiring

Full time

About AgZen:

AgZen is a fast-growing precision agriculture company headquartered in Somerville, MA, built on MIT research and focused on one problem: making crop spraying more efficient. Our flagship product, Real Coverage, is the world's first system that measures and controls droplet coverage at the leaf level, giving growers real-time visibility into spray performance and cutting chemical and water use by up to 50% without sacrificing yield.

We are a small, technically deep team working at the intersection of fluid mechanics, computer vision, AI, and real agricultural environments. If you want to build technology with measurable impact on how the world grows food, this is the place to do it.

About the Role

We are looking for a sharp, tenacious, and thorough Senior Machine Learning Operations (MLOps) Engineer to join our team. As part of the the perception team, you’ll own the operational layer around of machine learning models. This role will be responsible for the intake and leveraging crop protection data collected from Real Coverage units installed on sprayers all around the world which is then used improve our CV pipeline and Recommendation Engine.

This role will be an essential component of AgZen’s measurement focus group. Strong communication, flexibility, teamwork, the desire to take on different responsibilities and own them will all be essential skills for a successful applicant.

This role is located in Somerville, MA (Boston area) with work required to be in-person.

What You'll Do

Own the architecture, execution, and operational excellence of large-scale, cloud-native pipelines for multimodal sensor data ingestion, processing, labeling, and validation.

Champion model traceability by building a clear lineage for every production model. Track what data trained it, what code produced it, what validation it passed, and how it's performing. Evaluate and recommend tooling for versioning, metadata, and model registry

Partner with data scientists to detect data quality issues, detect drift in upstream sources, and ensure features stay fresh and reliable

Track model drift over weeks, flag slow degradation before it crosses a threshold, surface feature freshness problems before they cascade

Build diagnostic tooling to root cause pipeline and recommendation issues quickly. Ensure the right context is logged at each stage, candidates, features, serving context, and building the dashboards to tie it collectively

Own automated gates that block bad deployments and assist in running model issue retrospectives

Work with ML engineers, data engineers, and stakeholders to coordinate on post-deployment metrics, defining what metrics to collect after deployment and why they matter

Build tooling and support non-technical domain experts in understanding perception system performance and identifying opportunities for pipeline improvement

Collaborate closely with cross-functional teams of software engineers, machine learning scientists, product specialists, and researchers to design, build, and maintain robust data pipelines grounded in sound data organization, domain knowledge, and careful analysis

Communicate technical findings, data characteristics, and limitations clearly and effectively to both internal partners and external collaborators

What…