Applied Scientist

About Us

We are building transformer models focused on multilingual translation and custom transformer architectures. Our team works on large-scale NLP and transformer-based models with a strong focus on research, experimentation, model optimization, and production-grade AI systems.

We are looking for a highly technical Data Scientist who is passionate about developing and improving AI/ML models, working on transformer architectures, and contributing to advanced NLP research and development.

This is a core engineering and research role — not a prompt engineering or API integration role.

Role Overview

As a Data Scientist, you will work on the training, fine-tuning, evaluation, and optimization of transformer-based NLP systems and LLMs. You will collaborate closely with engineering and research teams to build scalable AI models and contribute to the development of advanced multilingual AI solutions.

You should be comfortable working with model training pipelines, datasets, tokenization techniques, transformer architectures, and GPU-based training environments. A strong interest in experimentation, model performance improvement, and real-world AI deployment is highly valued.

Key Responsibilities

• Lead the architecture and development of transformer-based AI systems
• Drive technical direction for NLP, LLM, and multilingual AI initiatives
• Mentor and guide junior ML engineers and researchers
• Train and fine-tune transformer models on large-scale custom datasets
• Work on Seq2Seq / encoder-decoder architectures for translation and text generation
• Optimize model performance using LoRA, QLoRA, PEFT, quantization, and distributed training techniques
• Design tokenization pipelines using BPE / SentencePiece
• Evaluate models using BLEU, perplexity, accuracy, and custom benchmarks
• Collaborate with platform teams for GPU infrastructure and scalable deployments

Required Qualifications

• 2+ years of experience in Data Science / Machine Learning / NLP
• Strong hands-on expertise in Transformers, LLMs, Seq2Seq Architectures, and Attention Mechanisms
• Proven experience in model training and fine-tuning using custom datasets
• Hands-on experience with Hugging Face, PyTorch, or TensorFlow
• Vector Databases: Pinecone, Milvus – for embeddings and semantic search in translation or LLM applications.
• Experience with distributed training, GPU optimization, and NLP evaluation metrics

Strongly Preferred

• Experience with LoRA / QLoRA / PEFT
• DeepSpeed / FSDP knowledge
• RLHF / SFT experience
• Multilingual model development
• Machine Translation systems
• Speech or sequence modeling systems
• Research/publication background in AI/ML/NLP

This Role Is NOT Focused On

• Prompt engineering only
• LangChain-only development
• OpenAI/GPT API integrations without model training
• RAG pipelines without core model development
• Low-code GenAI workflows

Tech Stack

• Python
• PyTorch / TensorFlow
• Hugging Face
• Transformers
• DeepSpeed / PEFT
• AWS / GPU Infrastructure
• Docker / Kubernetes
• Vector Databases & NLP Tooling

What We're Looking For

• Strong ownership and builder mindset
• Ability to independently lead research and experimentation
• Strong mentorship and leadership capabilities
• Deep understanding of transformer architectures
• Ability to bridge research and production AI systems