About the role
We are looking for a Senior Applied Scientist to help establish and lead the technical direction of our newly formed team in Bangalore. In this role, you will drive the research and development of next-generation machine learning models spanning computer vision, audio processing, and multimodal semantic understanding. You will help define the science roadmap, tackle high-ambiguity problems across modalities, and deliver solutions that operate at scale. This is a rare opportunity to shape the technical vision, culture, and long-term research agenda of a greenfield site.
Responsibilities:
- Model Development and Technical Leadership: Architect and drive development of advanced deep learning models for CV, audio understanding, and multimodal semantic fusion, setting the technical bar and defining best practices for the team.
- End-to-End Ownership: Own complex ML programs end-to-end from identifying high-impact problems, designing data strategies and evaluation frameworks, through experimentation, optimisation, and deployment at production scale.
- Research and Innovation: Define the science roadmap for your area; drive novel research directions in multimodal learning and deliver results that advance both the product and the broader field.
- Publications and Thought Leadership: Maintain an active publication record at top-tier venues (e. g., CVPR, NeurIPS, ICASSP, ICCV, ACL) and represent the team externally in the research community.
- Mentorship and Culture Building: Mentor scientists and engineers, raise the technical bar through hiring, and play a foundational role in establishing the Bangalore site's culture, processes, and scientific identity.
Requirements:
- PhD, or a Master's degree and 6+ years of applied research experience.
- 5+ years of experience building machine learning models for business applications.
- Experience programming in Java, C++, Python or a related language.
- 5+ years of hands-on experience building and deploying ML/DL models in computer vision, audio/speech processing, or multimodal learning.
- Proven track record of leading technical initiatives from conception to production impact.
- Deep expertise in modern neural architectures (transformers, diffusion models, foundation models).
- Experience defining science roadmaps and influencing cross-functional priorities.
- Familiarity with distributed training at scale, model compression, and low-latency inference.
- strong publication record at top-tier ML/CV/Audio conferences.
- Track record of mentoring junior scientists and building high-performing research teams.