Skip to content
Esc
Internships in Canada
New-grad roles in Canada
Remote roles
Roles that show pay
Postings at high ghost risk
PayWhat student tech roles in Canada pay
Check a linkPaste a posting to see its real age and ghost risk
JobsEvery open tech role
CompaniesWho fills fastest, who leaves postings up
Hiring calendarWhen employers open intern and new-grad roles
Skills gapWhat postings ask for that you don't have
Your resumeScore and fixes
Your matchesOpen roles ranked by your resume
ApplicationsSaved, applied, interviewing, offers
SettingsTheme, text size, motion
How it works

↑ ↓ to move, Enter to open? every shortcut

Keyboard shortcuts

CtrlKor/
Search employers, postings and pages
JorK
Next or previous posting in a list
O
Open the posting's page
Enter
Open it in the preview, on wide screens
S
Save or unsave it
?
This list
Esc
Close
Factored

Senior Machine Learning Engineer (RAGs)

  • Seen live on Factored's site today
  • Senior
Pay
Not stated
Location
Latin America
Open for
12+ days
First seen
Oct 23, 2026
Apply on Factored's site (opens in a new tab)

Posting record

No flags

Open 12+ days

The posting's record, oldest first
  1. Factored's site dates the posting
  2. Ghostless first sees this listing
  3. Last checked: still up
How we know

It hasn't been reposted, listed twice, or left up unusually long, and the posting doesn't say it's a talent pool.

Ghostless records the day it first sees a posting, so a repost can't reset its age. This one was already up when Ghostless started tracking Factored, so its true age is longer.

Last checked Nov 4, 2026.

How well do you match?

See how much of this posting your resume shows, and what it's missing.

Upload your resume

What it asks of you

5+ years of experience
“5+ years of hands-on experience developing and deploying machine learning models in production environments.” (quoted from the posting)

What the posting discloses

Not stated: Pay, real vacancy, AI screening.

Why these three

Ontario requires postings from employers with 25 or more staff to state the pay, whether the vacancy exists, and whether AI screens applicants.

Some employers add pay only on their own site. Check the original posting before you rule it out.

Skills mentioned

About the role

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 Senior 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

  • 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.

Something wrong with this posting?