Job Overview
Location
United States | Remote
Job Type
Full-time
Category
Software Engineer
Date Posted
May 21, 2026
Full Job Description
đź“‹ Description
- • Design, develop, and deploy custom deep learning models for computer vision analysis of dog and cat video footage to identify undiagnosed health conditions with specialist-level accuracy.
- • Implement novel neural network architectures optimized for video processing, including CNNs, RNNs, Transformers, and Vision Transformers, using PyTorch.
- • Collaborate with research and veterinary teams to translate cutting-edge academic papers into production-grade models that diagnose conditions from movement patterns and behavioral cues.
- • Build and maintain robust backend services and APIs using Go, Java, or Rust to support video ingestion, model inference, and veterinary interface workflows.
- • Develop full-stack applications with RESTful endpoints to enable veterinarians to upload, manage, and analyze pet videos via a secure, scalable web platform.
- • Deploy, monitor, and optimize ML models and supporting infrastructure in cloud environments (AWS/GCP/Azure) using containerization technologies.
- • Engineer end-to-end solutions from model training on GPU clusters to low-latency inference serving in live veterinary clinic settings.
- • Contribute to the development of a proprietary video capture platform that standardizes data collection for consistent, high-quality model inputs.
- • Ensure production systems meet reliability, scalability, and compliance standards for healthcare-adjacent applications handling sensitive pet health data.
- • Work cross-functionally with product, veterinary, and data teams to prioritize model improvements based on real-world clinical feedback and diagnostic accuracy metrics.
- • Iterate rapidly between research and engineering tasks—training state-of-the-art architectures one day and debugging HTTP endpoints the next.
- • Maintain clean, testable, and documented codebases across ML, backend, and frontend components to support team scalability and model reproducibility.
- • Continuously evaluate emerging research in animal behavior and computer vision to identify opportunities for model enhancement and new diagnostic capabilities.
- • Participate in model validation processes in partnership with large animal health companies to verify diagnostic performance against veterinary specialist benchmarks.
- • Contribute to the design of user-facing features that allow veterinarians to interpret model outputs and integrate them into clinical decision-making workflows.
🎯 Requirements
- • 3+ years experience developing and deploying computer vision models
- • Strong understanding of deep learning architectures for video processing (CNNs, RNNs, Transformers, ViTs)
- • Proficiency in PyTorch
- • Experience with backend development using Go, Java, Rust, or strongly typed languages
- • Working knowledge of web server technologies and RESTful API design
- • Familiarity with cloud infrastructure (AWS/GCP/Azure) and containerization
🏖️ Benefits
- • Opportunity to build technology that improves animal welfare at scale, impacting hundreds of millions of pets worldwide
- • Work with a team that has sustained over 35% annual growth for the past decade
- • Collaborate with leading animal health companies and veterinary experts
- • Remote work flexibility across the United States
- • Join a mission-driven company recognized on the Inc. 5000 every year since 2018
- • Engage in a dynamic, high-growth environment with exposure to both AI research and production engineering
Skills & Technologies
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About Synergy Pet Group
Synergy Pet Group is a veterinary services consolidator that acquires and partners with companion-animal practices across the United States. The company provides centralized administrative, marketing, purchasing, and technology support so veterinarians can focus on patient care while benefiting from shared resources and economies of scale. Established through private equity backing, it seeks to preserve clinic culture and clinical autonomy while improving operational efficiency and profitability.
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