Data Scientist / AI Scientist

Location: Remote
Job Type: Contract

Position Overview

We are seeking a highly experienced Data Scientist / AI Scientist with strong hands-on expertise in Generative AI, Large Language Models (LLMs), foundation model post-training, and machine learning.

The ideal candidate will have experience training and adapting foundation models using techniques such as Supervised Fine-Tuning (SFT), LoRA/QLoRA, Parameter-Efficient Fine-Tuning (PEFT), RLHF, DPO, and model distillation.

This is a hands-on technical leadership role requiring strong Python and deep learning skills, experience with multi-GPU/distributed training, model evaluation, cloud platforms, and production AI systems.

Key Responsibilities

Foundation Model Adaptation

• Define technical approaches for post-training and adapting foundation models.

• Design and implement training strategies including:

  • Supervised Fine-Tuning (SFT)

  • LoRA / QLoRA

  • Parameter-Efficient Fine-Tuning (PEFT)

  • Preference optimization such as RLHF and DPO

  • Knowledge/model distillation

• Develop reusable training recipes, reference architectures, and AI/ML tooling standards.

• Evaluate emerging model adaptation techniques and recommend approaches for adoption.

Hands-On Model Development

• Curate, clean, and prepare high-quality training and evaluation datasets.

• Design and execute fine-tuning experiments on multi-GPU infrastructure.

• Build reproducible ML experimentation and training pipelines.

• Track experiments, model versions, datasets, and evaluation results.

• Develop AI components and integrate them with backend and frontend systems to demonstrate end-to-end solutions.

Model Evaluation & Quality

• Establish model evaluation, benchmarking, and quality standards.

• Develop evaluation frameworks to measure model performance and improvement.

• Analyze model behavior and diagnose failure modes.

• Translate evaluation results into actionable decisions involving data, training methodologies, and architecture.

• Establish appropriate AI safety, reliability, and quality practices.

Technical Leadership

• Provide technical leadership and direction across AI/ML initiatives.

• Mentor AI scientists, machine learning engineers, and developers.

• Conduct architecture and design reviews.

• Establish engineering and ML best practices and technical standards.

• Collaborate with product managers, designers, researchers, engineers, and business stakeholders.

• Communicate technical concepts, trade-offs, costs, risks, and recommendations to senior and non-technical stakeholders.

Emerging AI Technologies

• Stay current with advances in:

  • Generative AI

  • LLMs

  • Foundation models

  • Model training and post-training

  • Agentic AI

  • AI evaluation and safety

• Evaluate emerging technologies and determine whether they should be adopted, piloted, or deprioritized.

• Share technical knowledge, research findings, and best practices across AI/ML teams.

Required Qualifications

• Strong hands-on experience with foundation model post-training and fine-tuning.

• Experience with one or more of the following:

  • Supervised Fine-Tuning (SFT)

  • LoRA / QLoRA

  • PEFT

  • RLHF

  • DPO

  • Knowledge distillation

• Strong proficiency in Python.

• Strong experience with deep learning frameworks such as PyTorch or TensorFlow.

• Hands-on experience with distributed and/or multi-GPU model training.

• Strong understanding of data curation, dataset preparation, experiment design, and model evaluation.

• Experience with ML/AI models in production environments.

• Experience with public cloud platforms and AI/ML cloud services such as AWS, Azure, or Google Cloud.

• Strong understanding of machine learning architecture and AI/ML tooling.

• Demonstrated experience providing technical leadership and mentoring engineers or data scientists.

• Excellent written and verbal communication skills.

• Ability to communicate complex AI/ML concepts and technical trade-offs to senior and non-technical stakeholders.

Preferred Qualifications

• Experience with Large Language Models (LLMs) and Generative AI.

• Experience with Hugging Face Transformers and related open-source LLM tooling.

• Experience with distributed training technologies such as DeepSpeed, FSDP, or similar frameworks.

• Experience with ML experiment tracking and model management tools such as MLflow or Weights & Biases.

• Experience with LLM evaluation, benchmarking, and AI safety.

• Experience developing AI agents or agentic AI systems.

• Experience with RAG and enterprise GenAI applications.

• Experience optimizing model training and inference performance and cost.

• Experience applying AI/ML to educational content, assessment, adaptive learning, or personalized learning.

Ideal Candidate

The ideal candidate is a hands-on AI/ML scientist and technical leader who has actually trained, fine-tuned, evaluated, and deployed machine learning or foundation models.

You should be comfortable moving from research and experimentation to production implementation, while also setting technical standards and mentoring other engineers and scientists.

Candidates should demonstrate strong technical judgment and the ability to determine when an AI solution is ready for production and when additional experimentation or improvement is required.