Agzen
Senior Perception Engineer
- Confirmed live in the last 24 hours
- $150k–$200k
- Senior and above
- Full-time
- On-site · Somerville, MA
- 5+ yrs exp
- Added today
About this role
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.
Written by Agzen. Original job post
Skills mentioned
- Deep Learning
- Machine Learning
- Numpy
- Pandas
- Python
- PyTorch
- SQL
- TensorFlow
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
Agzen
Likely interview questions
- Describe a time you designed experiments to analyze results and iterate quickly based on field requirements, as mentioned in the posting.
- Explain your experience architecting end-to-end computer vision pipelines and how you strategically decompose complex problems across multiple models.