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Lead Software Engineer
Dognosis
Software Engineering
Bengaluru, Karnataka, India
Posted on Jan 13, 2026
We're building technology that doesn't exist yet: brain-computer interfaces for dogs, real-time multi-modal sensor fusion, and automated cancer detection systems. Our 15+ person team spans neuroscience, computer vision, embedded systems, and animal behavior.
As we scale, we need stronger technical foundations. Our prototypes are innovative but need production-grade reliability. Our systems are cutting-edge but increasingly interdependent. We need someone who can architect robust solutions while keeping our research velocity high.
What You'll be Working On:
Technical Architecture & Integration – Design and implement the connective tissue between our ML pipelines, computer vision models, signal processing systems, and backend infrastructure. You'll write code, review architectures, and ensure components work together seamlessly.
Production-Grade Development – Build frameworks and patterns that help the team move from research prototypes to reliable, deployable systems. Write the infrastructure code that makes everything else possible: testing harnesses, CI/CD pipelines, monitoring systems, and deployment tools.
Code Quality & Reliability – Lead by example in writing robust, maintainable code. Champion best practices in testing, error handling, logging, and observability. Help the team shift from "make it work" to "make it work consistently."
Cross-Stack Problem Solving – Work across the entire stack—from ML model serving to cloud infrastructure to firmware integration. Debug complex issues that span multiple systems and help others do the same.
Technical Mentorship – Guide other engineers through code reviews, pair programming, and architecture discussions. Raise the technical bar without slowing down innovation.
What the Next 6 Months Have In Store
Stable integrations: ML and CV teams can deploy changes confidently without breaking other systems
Robust pipelines: Core workflows have comprehensive testing and <5% failure rates
Better velocity: Time from prototype to production deployment decreases by 30%+
Reduced incidents: Fewer emergency fixes, more proactive improvements
Technical foundation: Clear patterns and reusable infrastructure components in place
Who We're Looking For
Strong engineering fundamentals – You write clean, efficient code and understand system design, algorithms, and software architecture deeply.
Experience with complex systems – You've built software where multiple domains intersect: robotics, scientific computing, medical devices, autonomous systems, or similar. You're comfortable when ML meets hardware meets cloud infrastructure.
Production mindset – You care about reliability, observability, and maintainability. You know the difference between a demo and a system that runs in production.
Startup adaptability – You thrive in ambiguous environments where you help define requirements, not just implement them. You're comfortable wearing multiple hats.
Collaborative technical leadership – You influence through code quality and technical judgment, not authority. You make others around you better engineers.
Tech Requirements:
You'll work with:
ML/AI: PyTorch, TensorFlow, custom model serving infrastructure
Computer Vision: Real-time video processing, multi-camera systems
Backend: Python, cloud infrastructure (AWS/GCP), streaming data pipelines
Systems: Linux, Docker, embedded systems integration
Data: Time-series analysis, signal processing, large-scale data pipelines
What You Don't Need:
A PhD or research background (though scientific curiosity helps)
Domain expertise in neuroscience or veterinary medicine
Experience managing large teams (you'll mentor, not manage)
Every technology in our stack (you'll learn quickly)
You'll build the technical foundation for breakthrough innovations in animal health and neuroscience. The problems are genuinely challenging, the mission is meaningful, and you'll work with brilliant people across disciplines you've never encountered before.
Location: Bangalore, India (hybrid with lab work) |
Team: ~15 engineers, scientists, and specialists |
Reporting: R&D Lead (Co-founder)