In the AI era, being a strong engineer means more than writing great code. It means operating as both an architect and a team lead – designing the solution with clarity, then guiding AI agents to execute it at a level and speed that wasn't possible before. We are building a culture where this is the norm, and we're looking for someone who is excited to work and grow in that direction.
You will work across computer vision and algorithm disciplines – researching solutions, designing experiments, and delivering production-grade implementations – while collaborating closely with data engineering and product teams.
What You'll Do
Research and implement novel computer vision and algorithmic solutions for challenging real-world problems – spanning perception, learning, optimization, and graph-based reasoning.
Own algorithm quality end-to-end: design experiments, evaluate results, and drive iterations until production reliability targets are met.
Work across disciplines – sharing models, methods, and insights with algorithm, data engineering, and product teams.
Monitor production model health proactively; identify degradation early and lead remediation efforts.
Integrate AI tools into your workflow and grow into the role of architect and team lead – designing the solution, then directing agents to build it.
Raise the bar on the team's technical culture through code reviews, knowledge-sharing, and rigorous algorithmic thinking.
Relevant B.Sc./M.Sc. with 10+ years of experience as a Computer Vision or Algorithm Engineer, OR relevant Ph.D. with 7+ years of relevant industry experience.
Broad and deep computer vision background, with strong algorithmic foundations – comfortable working across CV and adjacent algorithm domains.
Deep proficiency in deep learning frameworks (PyTorch preferred).
Proficiency in Python – writes clean, production-grade code.
Proven E2E ownership: has taken CV problems from research to production, not just model experimentation.
Strong problem-solving skills – comfortable with ambiguous, non-textbook problems.
Self-motivated and entrepreneurial: identifies gaps, drives solutions, and takes accountability for outcomes.
Mentor and upskill team members, including knowledge sharing and strategic technical development.
Good communication skills – can clearly explain complex algorithmic decisions to both technical and non-technical stakeholders.












