- Location
- Latin America
- Department
- Machine Learning Engineering
- Experience
- 4+ years
- Source
- Greenhouse
Description
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 seeking a skilled Machine Learning Engineer to join our team, with a specialized focus on Retrieval-Augmented Generation (RAG) models. The ideal candidate will have experience in designing, developing, and deploying advanced machine learning models that integrate retrieval and generation capabilities to create powerful AI-driven applications.
Functional 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.
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.
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.
Skills
PythonAWSGCPMachine LearningDeep LearningNLPTensorFlowPyTorchData Science