We are looking for a Junior Research Scientist to work on foundation models for financial data. Several of them already run in production on bureau, transactional and graph data; your work is to extend that line to new data domains and new downstream tasks — credit risk, acquisition, anti-fraud, LTV forecasting.
We work with Python, PyTorch, MLFlow, AWS, Snowflake and modern AI coding tools such as Claude Code, OpenAI Codex and Cursor.
This is a great opportunity for someone at the beginning of their career and who wants to work on large-scale, technically challenging projects.
Challenges that await you:
- Train self-supervised models on discrete sequences to beat the SOTA and achieve business impact in downstream tasks across Plata, such as: credit risk, transaction anti-fraud, acquisition and LTV forecasting
- Stay on top of SOTA research, applying the latest NLP and DL techniques to fintech models
- Work with large multimodal datasets: tabular, behavioral, transactional, device and network, text, time series, graphs
- Optimize the utilization of compute resources for both training and inference
- Own and develop solutions end-to-end, from idea to data collection to experiments to training runs to inference optimization to evaluating impact
- Perform rigorous evaluations
- Write articles and speak at industry conferences
What makes you a great fit:
- Education: mathematics, engineering, computer science, artificial intelligence or another strong quantitative field
- Strong foundation in machine learning and deep learning, with hands-on experience building and experimenting with models using PyTorch and LLMs
- Understanding of dataset design and data mixing for deep learning training
- Demonstrated understanding of how to maximize the utilization of compute resources and efficiently perform experiments to validate research ideas
- Solid communication skills
- High autonomy and ownership
Your bonus skills:
- Published research in machine learning or deep learning
- Personal projects or startup experience in AI, machine learning or deep learning
- Internship experience in research, ML or DL teams
- B1 or higher English level for effective communication with an international team
Our ways of working:
- Innovative Spirit: A commitment to creativity and groundbreaking solutions
- Honest Feedback: valuing open, transparent communication
- Supportive Team: a strong, collaborative community
- Celebrating Achievements: recognizing our wins together
- High-Tech Environment: a team full of smart and revolutionary people who dare to challenge the status quo of incumbent finances
Our benefits:
- Relocation support to one of our hubs — Serbia, Georgia, Mexico or Colombia — with assistance for the employee and their family
- Flexible work from one of our offices or remotely within time zones from GMT-2 to GMT+5
- Healthcare Coverage
- Education Budget: Language lessons, professional training and certifications
- Wellness Budget: Mental health and fitness activity reimbursements
- Vacation policy: 20 days of annual leave and paid sick leave
Quantitative-field education and hands-on machine learning and deep learning experience with PyTorch and LLMs; requires dataset design, compute optimization, rigorous experimentation, communication, autonomy, and ownership.
Key Responsibilities
- training models
- evaluating models
- optimizing inference
Skills & Tools
Python, PyTorch, MLFlow, AWS, Snowflake, Claude Code, OpenAI Codex, Cursor, NLP, DL, LLMs
Job Details
- Category: Research and Development (R&D)
- Seniority: Entry Level
- Commitment: Full Time
- Workplace: Remote — Worldwide
- Languages: English
Benefits
- Generous paid time off
- Relocation assistance
About Plata Card
A financial technology company developing data-driven products for credit, payments, and fraud prevention. — Industry: Finance