Overview
Advanced Accelerator Program Machine Learning & GenAI — Latin America. Fully remote | Complete engagement job.
Factored helps U.S. companies build and scale AI, ML, and Data teams, powered by LATAM talent. Our mission is 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 growth is personal and ideas matter. We strive for excellence, celebrate diversity, encourage curiosity, and build an environment where you can thrive.
Program
The Advanced Accelerator Training Program is a high-impact initiative designed for candidates who want to master cutting-edge technologies and transition into high-level roles within Factored.
Highlights
- This is a rigorous, full-time commitment that bridges the gap between foundational skills and elite professional performance.
- You will work on capstone projects based on real-world problems, literature reviews, and more.
- Regular feedback sessions and lessons from industry experts focus on both technical (hard) and professional (soft) skills.
- Upon completion, you will be prepared to work with top-tier clients on technically demanding, high-impact AI projects.
Structure & Support
We provide the tools and environment you need to succeed from day one:
- Duration: Between 12 and up to 16 weeks, depending on training needs.
- Full-Time Commitment and exclusivity: ~45 hours per week.
- Note: A permanence clause applies. If you leave before completing two years, you must reimburse training expenses.
Qualifications
- Minimum 4+ years in the Machine Learning field with a proven track record of implementing ML solutions in production.
- Python Development: Proficiency in production-grade Python code.
- Deep Learning Fundamentals: Model architecture, forward propagation, loss functions, and optimization loops.
- NLP & Frameworks: Experience with NLP and PyTorch, TensorFlow, or Hugging Face to implement and fine-tune models.
- Backend & API Development: Experience building APIs using FastAPI or Flask.
- Deployment & Cloud: Containerization (Docker) and deploying ML models to AWS, Azure, or GCP.
- MLOps: Familiarity with CI/CD (GitHub Actions/GitLab CI), Model Versioning/Tracking (MLflow, Weights & Biases), Model Monitoring, and IaC.
- Excellent written and spoken English. Ability to lead in-depth technical discussions with engineers and communicate value to business stakeholders.
Program Logistics
- Initial Start date: February 16, 2026.
- Type of program: Full-time.
- Methodology: Mix of classroom and online content with extended discussions, literature review, capstone projects, and mentorship on soft-skills.
- Duration: 12 to 16 weeks.
- Stipend: Factored will pay a monthly stipend to all program participants.
- Fees: Factored does not charge upfront enrollment fees. Participants may sign a loan for participation cost, fully repaid and cancelled after 2 years of full-time employment with Factored.
Admission Process
- Online assessment: Applicants receive an online assessment with 2 weeks to complete. Preparation includes linear algebra, calculus, ML/DL fundamentals, Python basics, and algorithmic coding.
- Talent interview: Assess communication skills and technical concepts.
- Tech interview: Discuss practical and conceptual experience with tools and past projects.
- System design & cultural fit interview: Assess alignment with values, mission, culture, and design proficiency.
- 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 hire people who are supremely intelligent and talented, but also passionate about our mission and values such as honesty, diligence, collaboration, kindness, and a enjoyable work environment. We maintain a transparent workplace where everyone has a voice in building OUR company and where learning and growth are accessible based on merit.