Lead Data Scientist

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.