Location : Hybrid / Onsite
Team : AI / ML R&D
Type : Full-Time
We’re looking for a highly skilled, hands-on Data Scientist with 4–10 years of experience in applied AI / ML to join our fast-paced, research-driven team. This role demands deep expertise in transformer architectures along with strong fundamentals in model training, fine-tuning, and optimization.
You will work across text, audio, and video modalities , with the opportunity to specialize deeply in one area while maintaining the versatility to experiment across others. If you thrive in a startup-style, high-velocity R&D environment and enjoy taking ownership from architecture to deployment, this role is for you.
What You'll Do
Model Development & Fine-Tuning
- Run end-to-end experiments on transformer-based architectures (LLMs, Whisper, diffusion, LoRA, RLHF / SFT, multimodal models).
- Prototype, benchmark, and push the limits of emerging generative AI techniques.
Domain-Specific Applications
Audio
Lip-sync accuracyEmotional delivery (whispering, shouting, crying)Regional language model supportVideo
Character & scene consistencyQuality outputs comparable to Veo3 / SoraMotion smoothness & frame coherenceText
Extend LLMs to handle regional languagesBuild domain-adapted, fine-tuned modelsEvaluation & Optimization
Build automated evaluation pipelines for audio, video, and image quality scoring.Optimize trade-offs between speed, quality, and compute efficiency.Cross-Modality Integration
Experiment with audio–video synchronization.Integrate background score generation and text-to-video alignment.Push the boundaries of multimodal generative systems.Research & Experimentation
Stay ahead of rapidly evolving AI models, tools, and techniques.Test architectural variations and scaling strategies for production-ready systems.Ownership & Execution
Take complete ownership of projects from idea → prototype → deployment.Demonstrate strong problem-solving, accountability, and first-principles thinking.What You'll Need
Experience
4–10 years in applied ML / Data Science with significant generative AI experience.Core Fundamentals
Deep understanding of transformer architectures, training dynamics, and model optimization.Experience with LLMs, diffusion models, and multimodal architectures.Modality Expertise
Depth in at least one modality (text, audio, or video) with end-to-end project delivery.Technical Skills
Strong coding skills in Python .Expertise with frameworks like PyTorch or TensorFlow .Experience with deployment frameworks (e.g., FastAPI , model servers).Evaluation Experience
Ability to design and implement automated evaluation systems for generative outputs.Adaptability
Comfort with rapid experimentation and learning new tools, models, and techniques.