Data Scientist

Location: Hybrid/Onsite Team: AI/ML R&D Type: Full-TimeWe’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 DoModel Development & Fine-TuningRun 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 ApplicationsAudioLip-sync accuracyEmotional delivery (whispering, shouting, crying)Regional language model supportVideoCharacter & scene consistencyQuality outputs comparable to Veo3/SoraMotion smoothness & frame coherenceTextExtend LLMs to handle regional languagesBuild domain-adapted, fine-tuned modelsEvaluation & OptimizationBuild automated evaluation pipelines for audio, video, and image quality scoring.Optimize trade-offs between speed, quality, and compute efficiency.Cross-Modality IntegrationExperiment with audio–video synchronization.Integrate background score generation and text-to-video alignment.Push the boundaries of multimodal generative systems.Research & ExperimentationStay ahead of rapidly evolving AI models, tools, and techniques.Test architectural variations and scaling strategies for production-ready systems.Ownership & ExecutionTake complete ownership of projects from idea → prototype → deployment.Demonstrate strong problem-solving, accountability, and first-principles thinking.What You'll NeedExperience4–10 years in applied ML/Data Science with significant generative AI experience.Core FundamentalsDeep understanding of transformer architectures, training dynamics, and model optimization.Experience with LLMs, diffusion models, and multimodal architectures.Modality ExpertiseDepth in at least one modality (text, audio, or video) with end-to-end project delivery.Technical SkillsStrong coding skills in Python.Expertise with frameworks like PyTorch or TensorFlow.Experience with deployment frameworks (e.g., FastAPI, model servers).Evaluation ExperienceAbility to design and implement automated evaluation systems for generative outputs.AdaptabilityComfort with rapid experimentation and learning new tools, models, and techniques.