Senior ML Engineer - RAG & GenAI

Factored

United States

Remote

USD 180,000 - 280,000

Full time

4 days ago
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Benefits offered by this job

Equity participation
Annual company retreat
Education bonus
Winter break
Paid time off
In-person events
Tailored career roadmaps
High-performance culture

Job summary

Factored is hiring a Senior Machine Learning Engineer with a focus on Retrieval-Augmented Generation (RAG) to design, develop, and deploy cutting-edge AI solutions for high-profile clients in the United States. You will work across data, engineering, and leadership teams to deliver scalable RAG-powered applications and robust ML infrastructure.

The ideal candidate brings 5+ years of production ML experience, strong Python and deep learning fundamentals, and hands-on RAG expertise with frameworks

Qualifications

  • Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field.
  • 5+ years of hands-on experience developing and deploying machine learning models in production environments.
  • 4+ years of experience with production NLP and deep learning models using frameworks like PyTorch and TensorFlow.
  • At least 1+ year of experience with Retrieval-Augmented Generation (RAG) and other advanced techniques to optimize model performance.
  • Proven experience writing production-level code, with strong proficiency in Python.
  • Expertise in working with large language models (LLMs) such as GPT, Gemini, and Claude, along with proficiency in LLM frameworks like LangChain.
  • Strong understanding of prompting techniques, and the trade-offs between prompting and fine-tuning.
  • Experience with cloud platforms such as AWS or GCP (AWS preferred), or equivalent on-premise platforms.

Responsibilities

  • Design, develop, and optimize Retrieval-Augmented Generation (RAG) models that integrate retrieval-based and generation-based approaches to solve complex, real-world problems for our high-profile clients.
  • Improve the performance of RAG models through cutting-edge algorithms, innovative techniques, and model fine-tuning.
  • Collaborate with client Data and Engineering teams to establish and build robust machine learning infrastructure to meet project goals.
  • Work closely with leadership teams from our clients to identify and leverage AI/ML opportunities that can provide transformative solutions.
  • Fine-tune and adapt large language models (LLMs) for specific tasks and domains within the RAG framework.
  • Partner with cross-functional client teams to deploy RAG models into production environments, ensuring seamless integration and long-term success.
  • Apply advanced machine learning techniques, including LLMs, to develop effective AI solutions tailored to client needs.
  • Write clean, maintainable, and scalable code, ensuring all development is well-documented and testable.
  • Prioritize user experience and customer needs in all product development efforts.
  • Design and develop frameworks for GenAI products, such as search interfaces, chatbots, and summarization tools.
  • Build and implement machine learning models and algorithms that directly contribute to client growth and success through innovative, AI-driven solutions.
  • Provide technical leadership in identifying and evaluating AI/ML opportunities that empower clients to deliver exceptional solutions.

Skills

Production ML
NLP (Deep Learning)
Python
PyTorch
TensorFlow
LLMs (GPT,Gemini,Claude)
LangChain
Cloud (AWS/GCP)
Prompting techniques
Code quality & testing
RAG concepts

Education

Bachelor’s or Master’s in CS/Stats/Math

Job description

Factored is hiring a Senior Machine Learning Engineer with a focus on Retrieval-Augmented Generation (RAG) to design, develop, and deploy cutting-edge AI solutions for high-profile clients in the United States. You will work across data, engineering, and leadership teams to deliver scalable RAG-powered applications and robust ML infrastructure.

The ideal candidate brings 5+ years of production ML experience, strong Python and deep learning fundamentals, and hands-on RAG expertise with frameworks

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