Lead Data Scientist and Machine Learning Lead

Job Description

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

This role requires deep expertise in:

• 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.

Key Responsibilities

Architect & Build RLHF Frameworks

• Design end-to-end RLHF pipelines (SFT → Reward Modeling → PPO/DPO)

• Develop scalable human feedback collection systems

• Implement preference modeling and ranking pipelines

• Optimize reward models for multimodal outputs (image + text)

• Build automated evaluation frameworks

Train & Fine-Tune OSS Vision-Language Models

• Experience working with Qwen-VL, Llama, GPT OSS

• Pretraining / instruction tuning multimodal models

• Parameter-efficient fine-tuning (LoRA, QLoRA)

• Dataset curation & synthetic data generation

• Scaling training on multi-GPU / multi-node clusters

• Optimizing for alignment, hallucination reduction, and safety

Highly Scalable Deployment of VLM Systems

• Design distributed inference pipelines (GPU-optimized)

• Model serving using vLLM and Triton Inference Server

• Optimize latency, throughput, and cost

• Implement batching, KV caching, quantization, tensor parallelism

• Deploy on Kubernetes-based infrastructure

• Build monitoring for drift, performance, and hallucinations

Multimodal AI System Design

• Architect systems combining OCR, vision encoders, LLMs, retrieval

• Implement retrieval-augmented multimodal pipelines

• Design evaluation benchmarks for VQA, grounding, and reasoning

• Ensure model safety and guardrails

Technical Leadership

• Lead a team of ML engineers & research scientists

• Define technical roadmap for multimodal AI

• Review model architectures & code quality

• Collaborate with product and infrastructure teams.

Qualifications

• 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.