Lead AI Scientist

EdCast; Mountain View (CA); On-site; USD 190,000 - 270,000; Full time

Job summary

EdCast is seeking a Lead AI Scientist to drive research, development, and deployment of advanced AI/ML solutions across the organization. You will tackle complex business problems with Generative AI, LLMs, NLP, recommendations, and agentic AI, taking ideas from research to production with continuous optimization.

You will design and evaluate state-of-the-art techniques, develop and fine-tune foundation models, and collaborate with software, product, data, and platform teams to deliver

Qualifications

• Master's or PhD in CS/AI/ML/Statistics/Mathematics or related field.
• 7+ years of experience in AI/ML, data science, or related field.
• Strong understanding of ML and deep learning fundamentals.
• Hands-on Python and modern ML frameworks (PyTorch or TensorFlow).
• Experience deploying ML models at scale.
• Experience with Generative AI/LLMs, NLP, recommender systems, or IR.
• Experience with transformer architectures and foundation models.

Responsibilities

• Design and develop LLM, Generative AI, RAG, NLP, recommender, personalization, and agentic AI solutions.
• Research and evaluate state-of-the-art AI/ML techniques for business problems.
• Develop and fine-tune foundation models, embeddings, and task-specific models.
• Design training, fine-tuning, evaluation, and inference pipelines for AI models.
• Apply transformer architectures, RAG, embeddings, vector search, DL, RL, and info retrieval.
• Develop experimentation frameworks with metrics for model quality, relevance, accuracy, and cost.
• Investigate model behavior via experimentation, error analysis, and ablations.
• Build prototypes and transition approaches to production systems.
• Collaborate with SWE, Product, Data Eng, and Platform teams to productionize AI.
• Optimize models for accuracy, scalability, latency, memory, and cost.
• Design model evaluation frameworks including automated and LLM-based methods.
• Stay current with AI research and translate into practical solutions.
• Publish internal research, patents, or external papers when appropriate.

Skills

Analytical thinking

Communication

Problem solving

Statistical analysis

Education

Master's or PhD in CS/AI/ML/Statistics/Mathematics

Tools

PyTorch

TensorFlow

Python

Docker

Kubernetes

Spark

FAISS

Weaviate

Milvus

LangChain

Job description

We are looking for a Lead AI Scientist to drive the research, development, and deployment of advanced Artificial Intelligence and Machine Learning solutions across the organization. The role will focus on solving complex business and product problems using modern AI techniques, including Generative AI, Large Language Models (LLMs), NLP, recommendation systems, deep learning, reinforcement learning, and agentic AI. The ideal candidate combines strong scientific thinking with hands‑on engineering capabilities and can take AI ideas from research and experimentation through production deployment and continuous optimization.

In This Role You Will

• Design and develop LLM, Generative AI, RAG, NLP, recommendation, personalization, and agentic AI solutions.
• Research and evaluate state-of-the-art AI/ML techniques and determine their applicability to business problems.
• Develop and fine‑tune foundation models, domain‑specific models, embedding models, and task‑specific models.
• Design training, fine‑tuning, evaluation, and inference pipelines for AI models.
• Apply techniques such as transformer architectures, LLM fine‑tuning, RAG, embeddings, vector search, deep learning, reinforcement learning, contextual bandits, and information retrieval.
• Develop experimentation frameworks and establish metrics for model quality, relevance, accuracy, robustness, latency, and cost.
• Investigate model behavior through experimentation, error analysis, ablation studies, and statistical analysis.
• Build prototypes and proof‑of‑concepts and transition successful approaches into production systems.
• Collaborate with Software Engineering, Product, Data Engineering, and Platform teams to productionize AI solutions.
• Optimize models and AI systems for accuracy, scalability, latency, memory, and inference cost.
• Design and implement model evaluation frameworks, including automated, human, and LLM‑based evaluation.
• Stay current with advances in AI research and translate relevant research into practical solutions.
• Publish internal research, technical documentation, patents, or external research papers where appropriate.

You’ve Got What It Takes

• Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, or a related technical field.
• 7+ years of experience in AI/ML, Data Science, or a closely related field.
• Strong understanding of machine learning and deep learning fundamentals.
• Strong hands‑on experience with Python and modern ML frameworks such as PyTorch or TensorFlow.
• Experience developing and deploying machine learning models at scale.
• Strong experience with one or more of Generative AI/LLMs, NLP, recommendation systems, information retrieval, deep learning, reinforcement learning, or computer vision.
• Experience with transformer architectures and modern foundation models.
• Experience with model fine‑tuning, embeddings, vector databases, RAG, and LLM evaluation.
• Strong understanding of experimentation, statistical modeling, model evaluation, and hypothesis testing.
• Experience working with large‑scale datasets and distributed computing frameworks such as Spark.
• Strong analytical, communication, and problem‑solving skills.

Extra Dose of Awesome

• Experience with foundation models such as Llama, Mistral, Gemini, or comparable models.
• Experience with Hugging Face, LangChain/LangGraph, FAISS, Milvus, Weaviate, or similar AI technologies.
• Experience with model optimization techniques such as quantization, distillation, pruning, and parameter‑efficient fine‑tuning (PEFT/LoRA).
• Experience designing production‑grade AI platforms and services.
• Experience with Docker, Kubernetes, cloud AI/ML platforms, and MLOps.
• Experience with MLflow or similar experiment and model management platforms.
• Experience with AI evaluation frameworks and LLM‑as‑a‑Judge approaches.
• Research publications, patents, or contributions to open‑source AI projects.

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