Top Tech Jobs & Startup Jobs

Coordinate People Operations for employees across the Americas, including HRIS records, documentation, payroll data, onboarding, employee lifecycle administration, budgets, and employee relations case management. Advise managers on routine employee matters, maintain compliance documentation, and support policy updates. Coordinate Boston workplace operations, vendors, office supplies, and in-person, virtual, and hybrid events. Maintain confidentiality, improve processes, and collaborate across the People team.

Lead technical development of video-based computer vision and machine learning products for retail. Design end-to-end pipelines covering video ingestion, annotation, preprocessing, model training, evaluation, and deployment. Develop action recognition and suspicious behavior detection models, optimize precision, recall, and latency, and guide technical experiments in an ambiguous 0-to-1 environment. Collaborate with product and engineering stakeholders while shaping best practices and technical direction.

Lead technical development of production-grade computer vision and machine learning systems for video-based action recognition in retail. Responsibilities include designing end-to-end video ML pipelines, defining experiments, training and evaluating models, optimizing precision, recall, and latency, managing annotation workflows, and deploying privacy-aware solutions. The role also involves technical leadership, roadmap development, cross-functional collaboration, and translating ambiguous product challenges into robust commercial prototypes.

Lead technical direction and development of computer vision and machine learning systems for video-based action recognition in retail environments. Design data pipelines, annotation workflows, model training and evaluation processes, and production deployment strategies. Optimize precision, recall, latency, and privacy compliance while collaborating with product and engineering teams to deliver an end-to-end 0-to-1 solution.

Lead the technical direction and early development of video-based computer vision systems for retail action recognition. Design end-to-end data and ML pipelines, manage video annotation workflows, train and deploy production models, optimize precision, recall, and latency, and provide technical leadership while collaborating with product and engineering stakeholders.

Lead the technical direction and development of video-based computer vision and machine learning systems for retail. Design end-to-end pipelines, define experiments, build and evaluate action-recognition models, establish video annotation workflows, and deploy production-ready solutions. Collaborate with product and engineering stakeholders to translate business needs into technical roadmaps while optimizing precision, recall, latency, and privacy compliance.

Lead the technical direction and development of computer vision and machine learning systems for video-based action recognition in retail environments. Design end-to-end video data pipelines, annotation workflows, model training and evaluation processes, and production deployments. Optimize accuracy, latency, and privacy while collaborating with product and engineering stakeholders to validate assumptions, define technical roadmaps, and deliver production-ready prototypes.

Build and operate backend services, APIs, data models, web interfaces, and internal tools for identity verification products. Work across Python backend systems and TypeScript frontends, integrating cloud services, AI models, and on-device computer vision products. Own features end to end, including reliability, monitoring, security, privacy, and production delivery while using AI coding tools to prototype and harden solutions.

Design, train, evaluate, and deploy face detection, recognition, matching, liveness, and anti-spoofing models for high-assurance identity verification. Own data, benchmarks, training, debugging, and model integration while optimizing inference for mobile devices. Collaborate with C++, product, and QA engineers, track biometric research, define security-sensitive metrics, and mentor engineers.

Lead end-to-end development of face detection, recognition, matching, liveness, and presentation attack detection models for identity verification. Define benchmarks and quality metrics, manage data and labeling, optimize models for on-device inference, and integrate them with C++ SDKs across mobile platforms. Collaborate with product, engineering, and QA teams, track biometrics research, support product direction, and mentor other ML engineers.