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Home / Machine Learning Engineer (LLMs Knowledge Graphs)

Machine Learning Engineer (LLMs Knowledge Graphs)

  • Full Time
  • Anywhere

Factored

Fully remote | Complete engagement job

Founded in Palo Alto by Dr. Andrew Ng and Israel Niezen, Factored helps U.S. companies build and scale world-class AI, ML, and Data teams, powered by the top 1% of LATAM talent, with a defining purpose: To empower brilliant humans, unleash their potential, and amplify their impact in the world.

At Factored, you’ll be part of a community that values learning, ownership, and authenticity, where your growth is personal and your ideas matter. We’re transparent, curious, and collaborative. We strive for excellence, celebrate diversity, encourage curiosity, and build an environment where you can truly thrive.

We are looking for a Machine Learning Engineer to join our team,with a specialized focus on Knowledge Graphs and LLMs. You will drive the development of AI products for our clients and participate in the development of a top-notch AI. At Factored we are building a company that we all hold as our own, every single one of us. We need your skills to help take this rocketship to new heights and help create new opportunities for us. In return, you will be rewarded with an amazing team that supports you, rich culture, shared success and the flexibility to work– from the comfort of your home.

Functional Responsibilities:

  • Design and implement knowledge graph architectures using property graph (Neo4j)or RDF-based models.
  • Transform structured and semi-structured data into optimized graph structures and query them using Cypher or SPARQL.
  • Integrate knowledge graphs with LLMs using Retrieval-Augmented Generation (RAG) architectures to deliver actionable insights.
  • Build robust APIs (FastAPI) and application services to implement relationship strength analysis and network traversal logic.

Qualifications:

  • 5+ years of hands-on experience developing and deploying machine learning models in production environments.
  • Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related fields
  • Hands-on experience with knowledge graph technologies, specifically property graph (Neo4j) or RDFframeworks.
  • Proficiency in querying systems using Cypher and/or SPARQL.
  • Hands-on experience with AWS Neptune,GraphDB, or Memgraph for building production-grade Knowledge Graphs
  • Strong skills in ontology design and modeling complex entity relationships.
  • Expert-level Python development skills for building enterprise-grade applications.
  • Experience building Retrieval-Augmented Generation (GraphRAG) and AI-driven recommendation systems.
  • Strong knowledge of embeddings, vectordatabases, and semanticsearchtechniques.
  • Hands-on experience with major cloud platforms such as AWS, Azure, or GCP.
  • Experience working with FastAPI/Flask
  • Ability to integrate relational databases and diverse external data sources into a unified graph.
  • Excellent verbal and written communication skills in English.

Our Benefits:

  • Ownership through equity participation.
  • Annual company retreat.
  • Education bonus for continuous learning.
  • Company-wide winter break.
  • Paid time off.
  • Optional in-person events and meetups.
  • Tailored career roadmaps.
  • High-performance culture.
At Factored, we believe that passionate, smart people expect honesty and transparency, as well as the freedom to do the best work of their lives while learning and growing as much as possible. Great people enjoy working with other passionate, smart people, so we believe in hiring right, and are very selective about who joins our team. Once we hire you, we will invest in you and support your career and professional growth in many meaningful ways. We hire people who are supremely intelligent and talented, but we recognize that intelligence is not enough. Perhaps more importantly, we look for those who are also passionate about our mission and are honest, diligent, collaborative, kind to others, and fun to be around. Life is too short to work with people who don’t inspire you.
We are a transparent workplace, where EVERYBODY has a voice in building OUR company, and where learning and growth are available to everyone based on their merits, not just on stamps on their resume. As impressive as some of the stamps on our resumes are, we recognize that human talent and passion exist everywhere, and come from many backgrounds, so stamps matter much less than results. All of us are dedicated doers and are highly energetic, focusing vehemently on execution because we know that the best learning happens by doing. We recognize that we are creating OUR COMPANY TOGETHER, which is not only a high-performing fast-growing business but is changing the way the world perceives the quality of technical talent in Latin America. We are fueled by the great positive impact we are making in the places where we do business and are committed to accelerating careers and investing in hundreds (and hopefully thousands) of highly talented data science engineers and data analysts.
In short, our business is about people, so we hire the best people and invest as much as possible in making them fall in love with their work, their learning, and their mission. When not nerding out on data science, we love to make music together, play sports, play games, dance salsa, cook delicious food, brew the best coffee, throw the best parties, and generally have a great time with each other.

Originally posted on Himalayas

To apply for this job please visit himalayas.app.

About this role & career path

Working in Canada

Canada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, making it the second-largest country by total area, with the longest coastline of any country. Its border with the United States is the longest international land border. The country is characterized by a wide range of both meteorologic and geological regions. With a population of over 41 million, it has widely varying population densities, with the majority residing in its urban areas and large areas being sparsely populated. It

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