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:
As a Senior Perception Engineer, you will be a key contributor to AgZen’s sensing and software portfolio. This role is at the intersection of agrochemical spraying, machine learning, and computer vision, where you will own and develop the perception platform that helps growers make spraying more efficient. 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:
Develop advanced computer vision models that support real-time detection and decision-making in AgZen’s spraying systems.
Implement deep-learning models for tasks including object segmentation, classification, optical flow, and 3D reconstruction.
Architect and build new end-to-end models, potentially merging existing networks for improved performance.
Employ techniques like quantization to enable power-efficient model execution on edge devices.
Develop innovative methods to improve model accuracy and inference speed in edge deployment scenarios.
Collaborate with the software engineering team to architect and develop data processing and annotation pipelines for continuous system improvement.
Collaborate closely with hardware, firmware, and product teams to ensure vision models integrate seamlessly with system components.
Analyze field performance to identify root causes of model behavior, propose improvements, and validate updates through experimentation.
Establish best practices for versioning, testing, deployment, and continuous improvement of computer vision models.
Communicate progress, risks, and recommendations to engineering and product leadership in a clear and structured manner.
Stay at the forefront of AI research by exploring and applying advancements in CNNs, transformers, and model quantization.
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 in solving problems in computer vision/perception with a foundation in deep learning and ML
Experience working with real-time or edge-based inference and integrating models with hardware or embedded systems.
Demonstrated ability to design experiments, analyze results, and iterate quickly based on field and product requirements.
Deep experience training and validating computer vision models (e.g., object detection, segmentation, and classification) for robust performance in real-world environments.
Proven ability to architect end-to-end computer vision pipelines, strategically decomposing complex problems across multiple models and stages.
Expertise in MLOps best practices and tooling (e.g., MLFlow, W&B, or similar experiment tracking and deployment frameworks) for managing the entire model lifecycle from research to production at scale.
Advanced knowledge of software engineering principles, including software design, source control management, build processes, code reviews, and testing methods
Advanced scientific Python (NumPy, Pandas, scikit-learn) and hands-on experience with deep learning frameworks including PyTorch and/or TensorFlow
Adept with the latest research in perception and machine learning, demonstrated by activity in the research community (e.g., reading conference papers, publications).
Experience operating end‑to‑end data or ML pipelines for reliability, scale, and observability
Clear communication skills and the ability to work effectively with cross-functional teams in a fast-moving environment.
Preferred:
Master’s/PhD degree or equivalent experience in Computer Science, Robotics, or a related field.
Experience with the field of agriculture or related fields such as environmental or life sciences
Robust SQL skills and comfort digging into data distributions, feature health, and model behavior
Experience with data science based on real-world physical sensors data
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
Familiarity with embedded systems and optimizing models for edge devices.
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.
Skills Required
- Bachelor's or graduate degree in Computer Science, Electrical Engineering, or a closely related field
- 5+ years of experience solving computer vision or perception problems with a foundation in deep learning and machine learning
- Experience with real-time or edge-based inference and integrating models with hardware or embedded systems
- Ability to design experiments, analyze results, and iterate based on field and product requirements
- Deep experience training and validating computer vision models for object detection, segmentation, and classification in real-world environments
- Ability to architect end-to-end computer vision pipelines across multiple models and stages
- Expertise in MLOps practices and tooling such as MLflow, Weights & Biases, or similar frameworks
- Advanced knowledge of software engineering principles, source control, build processes, code reviews, and testing
- Advanced Python, including NumPy, Pandas, and scikit-learn
- Hands-on experience with PyTorch and/or TensorFlow
- Activity in the perception and machine learning research community, such as reading conference papers or publishing
- Experience operating end-to-end data or machine learning pipelines for reliability, scale, and observability
- Clear communication and effective cross-functional collaboration skills
- Master's or PhD degree, or equivalent experience, in Computer Science, Robotics, or a related field
- Experience in agriculture or related environmental or life sciences fields
- Robust SQL skills and experience analyzing data distributions, feature health, and model behavior
- Experience with data science involving real-world physical sensor data
- Experience creating intuitive data visualization tools for non-technical users
- Experience developing machine learning models related to biological or crop protection outcomes
- Familiarity with embedded systems and optimizing models for edge devices
What We Do
Each year, billions are spent on pesticides and fertilizers, yet most never reach the crops - resulting in costly waste and significant environmental harm. We’re changing that. By using machine learning, computer vision, and fluid dynamics, AgZen optimizes chemical application in real time, reducing input use, minimizing pollution, and protecting both yields and ecosystems. Founded out of MIT and backed by $13.5M in funding, our multidisciplinary team of engineers and ag science experts is redefining sustainable farming with breakthrough technology. We’re not just improving agriculture, we’re reimagining what’s possible.
Why Work With Us
AgZen combines MIT innovation with real-world farming, using AI and sensor tech to cut chemical use by up to 50% while improving results. Join us to scale breakthrough technology that’s transforming how growers spray, save, and sustain agriculture.
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