Worklog

Designed and shipped NexaCompute, a full end-to-end LLM distillation pipeline (data generation → filtering → training → deployment). Generated and curated a 100k scientific QA dataset using an async OpenAI pipeline with JSON-schema validation, then trained a 10B Falcon3 model with QLoRA (NF4, bf16, gradient checkpointing) on 2×A100 80GB. Achieved clean convergence on validation and test splits, released merged safetensors + model card + llm.txt, and published the distilled model for public inference—demonstrating a reproducible, low-cost method for building domain-specialized scientific assistants.

Built and open-sourced a 10B scientific reasoning model (Falcon3-10B QLoRA) and full training stack “NexaCompute.” Automated generation of 100k domain QA pairs (bio/physics/materials), filtered with strict schema-based QA, then trained on 2×A100 with reproducible manifests, checkpoints, and W&B tracking. Achieved strong convergence on hold-out data and published merged weights for real-world inference—showing how large models can be cheaply specialized into high-fidelity scientific assistants.

Designed and delivered custom machine learning solutions for individual clients across finance and AI research.

• Developed reinforcement-learning-from-human-feedback (RLHF) fine-tuning pipelines for open-source LLMs.
• Built and deployed lightweight crypto-trading models using Python, PyTorch, and on-chain market data.
• Implemented automated data ingestion and preprocessing pipelines for personal research dashboards.
• Provided reproducible documentation and experiment tracking (Weights & Biases, Hugging Face Hub).

Contributed to the development of end-to-end ML and research pipelines at an early-stage AI startup. Designed and documented internal systems for model distillation, evaluation, and deployment while collaborating with senior engineers to streamline data workflows, automation, and reproducibility across the stack.

• Built and maintained the model distillation framework from data generation to evaluation and deployment.
• Authored two major internal technical wikis:
  • A comprehensive guide to the modern fine-tuning ecosystem (Swift, TRL, Megatron-LM, RL frameworks).
  • A deep-dive into distributed training protocols and scaling challenges beyond 30B parameters.
• Conducted performance benchmarking and API-driven model evaluations; improved internal inference runtime from 20 minutes → 2 seconds through low-level argument tuning and process optimization.
• Automated large-scale evaluation pipelines and documented production workflows for remote development, SSH environments, and code review cycles.
• Collaborated cross-functionally on synthetic data generation for distillation pipelines, ensuring alignment between research output and production infrastructure.
• Integrated and optimized inference pipelines using vLLM and related frameworks for large-scale evaluation and model serving.

This role provided extensive exposure to production-grade ML systems, distributed training, and the realities of scaling AI research infrastructure in startup environments.

Trained and benchmarked multiple model architectures (Transformers, CNN-BiLSTM hybrids, diffusion VAEs, etc.) on structured and scientific datasets.

• Used Kaggle’s GPU runtime for rapid prototyping of scientific ML experiments before scaling them to NexaCompute or Hugging Face.
• Developed and validated preprocessing pipelines (tokenization, normalization, augmentation) and verified consistency before dataset release.
• Logged and tracked experiment performance using Weights & Biases (W&B) integration for reproducibility.
• Applied aggressive optimization strategies (e.g., efficient batching, precision tuning, caching) to minimize runtime and compute cost.