Senior Computer Vision & Machine Learning Engineer

Quick Facts - Role: Senior Computer Vision & Machine Learning Engineer Description Own and advance computer vision capabilities for a digital holographic microscope, from benchmark design and model development through integration, evaluation, and production-quality software. Develop, train, and deploy models for detection, multi-object tracking, motility measurement, and image-quality assessment, using rigorous evaluation that separates detector/tracker/reconstruction and sample-quality failures. Contribute to device-to-cloud data handling and integration testing as priorities shift. Responsibilities - Take technical responsibility from problem definition and benchmark design through algorithm/model development, integration, and quantitative evaluation. - Develop and deploy PyTorch-based models for sperm detection, multi-object tracking, motility measurement, and image-quality assessment. - Design evaluation systems with subject-disjoint splits, versioned model artifacts, clinically relevant metrics, and detailed error analysis. - Select classical vs deep vs hybrid approaches based on measured results. - Define annotation requirements, labeling specifications, and ground-truth standards; collaborate to build and maintain datasets. - Carry models through GPU profiling, batch inference optimization, testing, deployment, and code review. - Support device acquisition software, device-to-cloud data handling, cloud batch workers, APIs, and integration testing when needed. - Establish repeatable training workflows (versioned configs, experiment tracking, provenance, traceable performance). - Use engineering automation/coding agents to accelerate work while critically reviewing generated changes. - Investigate failures in dense clusters, crossings, debris, drift, blur, and reconstruction artifacts. - Work from incomplete specifications to define a practical first step and required inputs. Requirements - Demonstrated experience shipping production-real computer vision or image-processing systems with responsibility beyond offline modeling (integration/testing/deployment/monitoring/operation). - Strong Python skills and hands-on PyTorch (or comparable framework). - Experimental rigor: dataset splitting, metric selection, ablations, reproducibility, and error analysis (including guarding against leakage and misleading metrics). - Software engineering fundamentals: typed interfaces, unit/integration testing, version control, code review, and technical documentation. - Ability to define what must be measured/built when goals are incompletely specified. - Proven ability to complete difficult technical work via integration, review, and delivery. - Comfortable using coding agents as engineering tools while retaining technical accountability. - Clear English communication with technical and non-technical collaborators. - MS or PhD in CS/EE/Biomedical or related field, or equivalent experience (typically 5+ years for senior candidates). Benefits - B2B contract - Fully remote with flexible hours and reliable overlap with US Eastern Time - Flexible paid time off formatted_html_description -

Quick Facts

• Role: Senior Computer Vision & Machine Learning Engineer

Description

Own and advance computer vision capabilities for a digital holographic microscope, from benchmark design and model development through integration, evaluation, and production-quality software. Develop, train, and deploy models for detection, multi-object tracking, motility measurement, and image-quality assessment, using rigorous evaluation that separates detector/tracker/reconstruction and sample-quality failures. Contribute to device-to-cloud data handling and integration testing as priorities shift.

Responsibilities

• Take technical responsibility from problem definition and benchmark design through algorithm/model development, integration, and quantitative evaluation.
• Develop and deploy PyTorch-based models for sperm detection, multi-object tracking, motility measurement, and image-quality assessment.
• Design evaluation systems with subject-disjoint splits, versioned model artifacts, clinically relevant metrics, and detailed error analysis.
• Select classical vs deep vs hybrid approaches based on measured results.
• Define annotation requirements, labeling specifications, and ground-truth standards; collaborate to build and maintain datasets.
• Carry models through GPU profiling, batch inference optimization, testing, deployment, and code review.
• Support device acquisition software, device-to-cloud data handling, cloud batch workers, APIs, and integration testing when needed.
• Establish repeatable training workflows (versioned configs, experiment tracking, provenance, traceable performance).
• Use engineering automation/coding agents to accelerate work while critically reviewing generated changes.
• Investigate failures in dense clusters, crossings, debris, drift, blur, and reconstruction artifacts.
• Work from incomplete specifications to define a practical first step and required inputs.

Requirements

• Demonstrated experience shipping production-real computer vision or image-processing systems with responsibility beyond offline modeling (integration/testing/deployment/monitoring/operation).
• Strong Python skills and hands-on PyTorch (or comparable framework).
• Experimental rigor: dataset splitting, metric selection, ablations, reproducibility, and error analysis (including guarding against leakage and misleading metrics).
• Software engineering fundamentals: typed interfaces, unit/integration testing, version control, code review, and technical documentation.
• Ability to define what must be measured/built when goals are incompletely specified.
• Proven ability to complete difficult technical work via integration, review, and delivery.
• Comfortable using coding agents as engineering tools while retaining technical accountability.
• Clear English communication with technical and non-technical collaborators.
• MS or PhD in CS/EE/Biomedical or related field, or equivalent experience (typically 5+ years for senior candidates).

Benefits

• B2B contract
• Fully remote with flexible hours and reliable overlap with US Eastern Time
• Flexible paid time off