Senior Machine Learning Operations Engineer

Agzen; Somerville, Northern (MA, KY); Hybrid; USD 150,000 - 200,000; Full time

Benefits offered by this job

Immediate impact

Early-employee equity

401(k) with employer matching at 6 m

6 weeks PTO per year

12 paid holidays

Medical, Dental and Vision insurance

Job summary

AgZen, a fast-growing precision agriculture company in Somerville, MA, is hiring a Senior Machine Learning Operations Engineer to own cloud-native data pipelines and model governance for RealCoverage. You’ll work with a small, technically deep team spanning hardware, software, and agronomic science, contributing to farming efficiency and sustainability.

You will lead end-to-end ML operational workflows, oversee data ingestion, labeling, validation, and model deployment, and collaborate with data

Qualifications

• Bachelor’s or graduate degree in Computer Science, Electrical Engineering, or a closely related field.
• 5+ years of experience building large-scale distributed systems, applications, or advanced ML systems.
• Experience with MLOps, data pipelines, and cloud distributed systems.
• Proficiency in Python for system-level and performance-critical implementation.
• Experience operating end-to-end data or ML pipelines for reliability, scale, and observability.
• Communication skills that align collaborators and drive execution across functions.
• Familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow).
• A record of ownership, accountability, and customer-focused engineering.
• Proven track record of designing robust frameworks with high-quality, durable APIs.
• Deep understanding of machine learning algorithms with hands-on application.
• Expertise in building reliable, high-performance, and cost-efficient systems on modern cloud infrastructure.

Responsibilities

• 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, including data, code, validation, and performance.
• Partner with data scientists to detect data quality issues and drift, ensuring features stay fresh and reliable.
• Track model drift and surface feature freshness issues to prevent cascading problems.
• Build diagnostic tooling to quickly root cause pipeline and recommendation issues and log rich context.
• Own automated gates that block bad deployments and facilitate post-deployment retrospectives.
• Collaborate with ML engineers, data engineers, and stakeholders on post-deployment metrics and data collection rationale.
• Build tooling to help non-technical domain experts understand perception system performance and pipeline improvements.
• Work with cross-functional teams to design, build, and maintain robust data pipelines grounded in domain knowledge.
• Communicate technical findings and limitations clearly to internal partners and external collaborators.

Skills

MLOps

Python

Distributed systems

Cloud computing

Data pipelines

SQL

Deep learning

Communication

PyTorch

TensorFlow

Education

Bachelor’s degree in CS/EE

Tools

NumPy

Pandas

scikit-learn

PyTorch

TensorFlow

Job description

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

Why AgZen

A place to do your best work

Real-World Impact

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.

Hard Problems, Fast Pace

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

Open Positions

Senior Machine Learning Operations Engineer

Location

Employment Type

Full time

Department

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, RealCoverage, 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 RealCoverage 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 We're Looking For

Required:

• Bachelor’s or graduate degree in Computer Science, Electrical Engineering, or a closely related field
• 5+ years of experience building large-scale distributed systems, applications, or advanced ML systems‑scale distributed systems, applications, or advanced ML systems
• Experience with MLOps, data pipelines, and cloud distributed systems
• Proficiency in Python for system‑level and performance‑critical implementation
• Experience operating end‑to‑end data or ML pipelines for reliability, scale, and observability
• Communication skills that align collaborators and drive execution across functions
• Familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow)
• A record of ownership, accountability, and customer‑focused engineering
• Proven track record of designing robust frameworks with high-quality, durable APIs
• Deep understanding of machine learning algorithms with hands‑on application
• Expertise in building reliable, high-performance, and cost‑efficient systems on modern cloud infrastructure‑performance
• Robust SQL skills and comfort digging into data distributions, feature health, and model behavior

Preferred:

• Experience with the field of agriculture or related fields such as environmental or life sciences
• Experience with data science based on real-world physical sensors data
• Experience with vision-based ML
• Experience creating intuitive data visualization tools that make complex data approachable for non-technical users
• Prior experience in developing machine-learning models relevant to biological or crop protection outcomes
• Advanced scientific Python (NumPy, Pandas, scikit-learn) and hands‑on experience with PyTorch and/or TensorFlow, including training and deploying neural networks
• Experience operating recommendation systems at scale

What We Offer

• The opportunity to make an immediate and visible impact in a fast-growing company
• Early-employee equity
• 401(k) with employer matching at 6 months of employment
• 6 weeks of PTO per calendar year
• 12 paid holidays
• Medical, Dental and Vision insurance

The salary range for this position is $150,000 - $200,000 depending on skills and qualifications evaluated on a per candidate basis.