Lead Data Scientist

We are looking for a Lead Data Scientist - Vision & Multimodal AI to architect and build next-generation Vision-Language Model (VLM) systems at scale.

The Candidate Will Have Responsibilities Across The Following Functions

Architect and Build RLHF Frameworks:

• Design end-to-end RLHF pipelines (SFT Reward Modelling PPO/DPO).
• Develop scalable human feedback collection systems.
• Implement preference modelling and ranking pipelines.
• Optimise reward models for multimodal outputs (image + text).
• Build automated evaluation frameworks.
  
  

• Experience working with Qwen-VL, Llama, and GPT OSS.
• Pretraining / instruction tuning multimodal models.
• Parameter-efficient fine-tuning (LoRA, QLoRA).
• Dataset curation and synthetic data generation.
• Scaling training on multi-GPU / multi-node clusters.
• Optimising for alignment, hallucination reduction, and safety.
  
  

• Design distributed inference pipelines (GPU-optimised).
• Model serving using vLLM and Triton Inference Server.
• Optimise latency, throughput, and cost.
• Implement batching, KV caching, quantisation, and tensor parallelism.
• Deploy on a Kubernetes-based infrastructure.
• Build monitoring for drift, performance, and hallucinations.
  
  

• Architect systems combining OCR, vision encoders, LLMs, and retrieval.
• Implement retrieval-augmented multimodal pipelines.
• Design evaluation benchmarks for VQA, grounding, and reasoning.
• Ensure model safety and guardrails.
  
  

• Lead a team of ML engineers and research scientists.
• Define a technical roadmap for multimodal AI.
• Review model architectures and code quality.
• Collaborate with product and infrastructure teams.
  
  

• 6+ years in ML / AI.
• 2+ years working with large-scale LLM or VLM systems.
• Strong hands-on experience building RLHF pipelines (not just using libraries).
• Deep PyTorch expertise.
• Experience training models > 7B parameters.
• Experience with distributed training (Deep Speed, FSDP).
• Production-grade deployment experience handling 10k+ QPS workloads.
• Strong understanding of transformer architectures.
  
  

• Architecting and implementing RLHF (Reinforcement Learning from Human Feedback) Frameworks.
• Training and fine-tuning Open-Source Vision-Language Models (VLMs).
• Deploying and scaling multimodal models to production, serving millions of requests.