Senior Computer Vision Engineer

About Diffraqtion

Diffraqtion builds a novel Quantum Vision System that pulls more information out of each photon than conventional imaging allows. Our photonics process incoming light before it reaches the sensor, so the computation happens passively in the light itself. We are an early-stage team in Somerville by founders from MIT and Harvard, working under NASA and DARPA contracts and backed by Lockheed Martin and Sumitomo. Our systems are headed for satellites that protect assets in orbit and on the ground, and for telescopes hunting Earth-like planets around other stars. You would be joining early, with real hardware to build and real missions to put it on.

The Role

We are hiring a Computer Vision Engineer to build the models at the center of our Quantum Vision System. Our photonics do part of the computation in light before it reaches the sensor and your models finish it. You will own the imaging pipeline from raw sensor output to actionable detections: physics-based reconstruction and de-noising, detection and tracking, and deployment on the embedded hardware that flies. You will work directly with our optical and opto-mechanical engineers to build an end-to‑end model of the imaging chain, from photons to outputs, so optics and algorithms can be optimized together. The first systems you work on are headed to space.

You have built multi-sensor fusion on real sensors and have shipped estimators and learned models that had to work on hardware, not just on benchmarks. You are comfortable with probabilistic models and deep learning in the same week. You want a problem nobody has solved yet and the hardware to test it on.

Note : This position requires U.S. citizenship, lawful permanent resident status, or eligibility for authorization under U.S. export control regulations (ITAR/EAR).

Key Responsibilities

• Design, train, and deploy computer vision models for imaging and perception tasks like image reconstruction, detection, classification, and tracking tailored to our optical front end.

• Develop de-noising and image-restoration methods (classical and learned) that are robust to sensor noise, radiation effects, low light, and optical aberrations.

• Build a cohesive, end-to‑end model of the imaging chain coupling physics-based optical and sensor models with neural networks to enable joint optimization of optics and algorithms.

• Design and implement sensor fusion algorithms that combine camera data with other onboard sensors using probabilistic and learned approaches.

• Integrate vision and fusion outputs into the system’s real‑time control loop, defining interfaces, latency budgets, and failure modes together with controls and embedded engineers.

• Optimize and deploy models to resource‑constrained edge hardware (e.g., embedded GPU, FPGA, or SoC), including quantization, pruning, and runtime profiling.

• Build data pipelines and simulation tools for synthetic and lab‑captured datasets, including calibration, labeling, and domain‑gap analysis between simulation and hardware.

• Define metrics and run hardware‑in‑the‑loop experiments in the lab to validate algorithm performance on real sensor data.

• Document designs and results clearly, and contribute to technical reviews and the team’s shared codebase and best practices.

Qualifications

Required:

• M.S. in Computer Science, Electrical Engineering, or a related engineering field.

• 3+ years of industry experience developing and deploying computer vision or perception systems on real sensors.

• Hands‑on experience with multi‑sensor measurement fusion: combining measurements from multiple imaging or onboard sensors into one estimate or image, using probabilistic and learned methods.

• Hands‑on experience with computational imaging: image de‑noising, restoration, or reconstruction.

• Strong grounding in estimation theory and inverse problems and hands‑on experience with deep learning for imaging.

• Experience designing experiments that hold up: ablations, baselines, uncertainty quantification.

• Strong proficiency in Python and PyTorch (or equivalent deep learning framework), with working knowledge of C++.

• Solid foundation in image formation, camera models, and calibration, and in the noise characteristics of imaging sensors.

• Solid software engineering practices: version control, testing, and reproducible experiments.

• This an on‑site in Somerville, MA, alongside the hardware team.

Preferred:

• Background in aerospace, defense, robotics, or autonomous systems, ideally with space or airborne sensors.

• Robotics state estimation: Kalman filters, factor graphs,