Image Processing and Analysis Engineer

Merge Labs is a frontier research lab with the mission of bridging biological and artificial intelligence to maximize human ability, agency and experience. We’re pursuing this goal by developing fundamentally new approaches to brain-computer interfaces that interact with the brain at high bandwidth, integrate with advanced AI, and are ultimately safe and accessible for anyone to use.

We turn physical signals into information and back. We reason backward from neural readout and stimulation goals to co-optimize the physical and computational systems that make them possible. Working with scientists and engineers, we combine simulation, measurement, and signal processing across neural interfaces, device characterization, and high-throughput biological screening. Our systems stream tens of gigabits per second. Within constrained power envelopes, we find what limits sensitivity, resolution, and reliability, push what today’s devices can deliver, and set the specifications for future generations.

You will build and maintain a shared image-analysis core that turns image series into reliable measurements across our platforms, with tools scientists can use independently. Image registration is central to the role, both across image series and to anatomical references. Early work includes locating wells and identifying brain imaging planes to guide repeatable probe positioning, and extracting response measurements for screening and in-vivo imaging.

• Develop image-based methods and scan strategies to locate wells and match brain imaging planes for repeatable probe positioning.

• Build analysis workflows for object and peak detection, background subtraction, drift correction, and response metrics such as rise and fall times.

• Develop and validate registration methods for reconstructed image series, quantifying motion and alignment accuracy.

• Develop registration methods to align ultrasound images with MRI and other anatomical references for imaging and targeting workflows.

• Agree metric definitions and acceptance criteria with scientists, including accuracy, repeatability, and failure conditions.

• Rigorously validate analysis methods with reference measurements, controls, and regression tests, assessing signal preservation and repeatability.

• Improve and maintain tested, documented Python tools that scientists can run independently.

• Integrate analysis methods with acquisition and reconstruction workflows and optimize runtime and memory use for large image datasets.

• Experience developing or substantially improving quantitative image-analysis workflows used by others.

• Strong practical image-processing and spatial-reasoning skills, including detection and image registration.

• Sound numerical and signal-processing judgment about how sampling, filtering, and correction affect measured responses.

• Strong scientific Python skills and experience maintaining tested, documented software.

• Practical judgment about runtime and memory use in image-analysis workflows.

• A disciplined approach to validation and honest reporting of uncertainty, limitations, and failures.

• Experience turning ambiguous scientific requests into useful workflows with the people who use them.

• Prior experience with multimodal image registration, particularly alignment to MRI.

• Ultrasound, microscopy, or other scientific imaging.

• Image-guided experimental automation.

• GPU or distributed image processing.

• Visualization tools used by scientists.

• Developing and evaluating learned image-analysis methods.

• Experience turning scientific Python prototypes into production-quality software, including work with build systems such as Bazel.

• Experience using AI tools or…