Location: Hybrid (India)
Experience Range: 1 - 2 Years
Company Location: Surat, Gujarat
Education Background: BE/BTech in CS/IT
About VideoSDK:
At VideoSDK, we’re building the real-time intelligence layer for the next generation of applications — fast, private, multimodal, and on-device.
We're hiring an AI Research Engineer to lead infrastructure development for small language models (SLMs) and speech systems, optimized for on-device multimodal AI. Your work will directly power products like real-time voice agents, live translation, intelligent video systems, and low-latency assistants — all running beyond the cloud.
Build scalable infrastructure to train, evaluate, and deploy small language models efficiently.
Design and implement RL-based training loops (e.g., PPO, DPO, RLAIF) for tuning small models in constrained environments.
Work with speech systems, including speech-to-text, text-to-speech, and voice activity detection.
Optimize models for on-device inference – targeting mobile, browser, and edge hardware (CPU, GPU).
Contribute to building real-time multimodal AI pipelines combining text, audio, and video.
Translate cutting-edge research into clean, production-ready code.
Drive experiments on quantization, distillation, and architecture search for efficient deployment.
Strong understanding of training, testing, and fine-tuning small models (≤2B parameters).
Experience with reinforcement learning for language models (GRPO, PPO).
Proven experience working with speech models (Whisper, xTTS, Silero etc).
Ability to read and implement research papers quickly and effectively.
Solid Python and PyTorch skills with an eye for clean, modular code.
Experience with building and managing custom datasets, training pipelines, and evaluation suites.
Familiarity with multimodal AI, particularly combining audio, text, or video.
Bonus Points
Experience optimizing inference for mobile or edge deployments (ONNX, CoreML, TFLite, MLX, WebAssembly)
Knowledge of state space models, recurrent architectures, or other attention-free designs.
Contributions to open-source AI projects.
Familiarity with latency-constrained or real-time systems.
We’re pushing the boundaries of real-time, personalized, multimodal AI — and doing it on the edge. You’ll work in a fast-moving environment where research meets product, and where your work directly shapes next-gen communication, assistants, and live experiences.
This is your chance to build the infrastructure behind real-time, human-like AI systems that don’t just live in the cloud — they live everywhere.