Senior Computer Vision Engineer

Job Summary

As a Senior Computer Vision Engineer, you will be a cornerstone of our technical team, responsible for the end-to-end lifecycle of our core perception systems. You’ll architect, own, and deliver fault-tolerant CV/ML pipelines that form the bedrock of our product. This role demands mastery of technical trade-offs — balancing algorithmic complexity against the harsh realities of production latency, reliability, and compute constraints. You’re not just building models; you’re engineering robust perception systems for mission-critical applications.

Job Roles & Responsibilities

• Architect & Own End-to-End Vision Systems: Lead the design and implementation of complex pipelines for tasks such as object detection, instance segmentation, industrial anomaly detection, and vision-based reasoning - ensuring high reliability and low latency
• Drive Project Execution & Technical Roadmap: Translate ambiguous business goals into a clear technical strategy and execution plan. Own major modules from concept to deployment, manage timelines, and proactively eliminate roadblocks.
• Bridge Research and Production: Spearhead the transition of algorithms from Python/PyTorch experimentation to optimized C++ implementations running on edge GPUs via tools like TensorRT, ONNX, and GStreamer pipelines.
• Collaborate Across Functions: Serve as a key technical voice in cross-disciplinary discussions with product managers, sales teams, and systems engineers to define and refine what’s technically feasible.
• Define Long-Term Data & Model Strategy: Influence core strategy for data acquisition, synthetic data generation, active learning, and the creation of robust validation frameworks that reflect real-world edge cases.
• Act as a Force Multiplier: Mentor fellow engineers through exemplary code, rigorous design reviews, and clear documentation. Be a technical anchor who elevates the team and sets a high bar for engineering excellence

Cultural Expectations

• Familiarity with NVIDIA DeepStream and GStreamer frameworks.
• Experience with LLMs, VLMs, or agentic AI systems.
• Contributions to open-source CV/ML projects.

Hiring Process

• Interview with hiring manager to assess technical skills.
• Live coding task to evaluate coding proficiency.
• Technical interview with senior member from the technical team.
• Final interview with Tech Lead/CTO.