About CloudWalk
We are a team of innovators and technologists building the future of payments. With millions of happy customers, we’re expanding our capabilities to bring justice to the broken payment system.
Our mission is to create a smarter, AI-driven future for payments—together.
The R&D Team
* We love data and have access to an immense corpus of shared knowledge and incredible statistical and computational tools.
* We like to explore before we exploit.
* We sprinkle sci-fi references in everything we do.
Job Description
You will be part of an exploration team inside the AI department. We take a step back from the day-to-day urgencies and pursue ambitious projects with high impact potential.
You will join our efforts to discover and refine transformer-based neural network architectures, data pipelines, pre-training objectives, and fine-tuning strategies that will power the next generation of machine-learning models for finance.
You’ll have access to tons of data, but you’ll also be cursed with tons of noise. Everyday events generate a torrent of perfectly normal behavior. To find truly valuable insights, you will need to follow clues, spot patterns, ask sharp questions, wrestle with uncertainty, uncover the story hidden in the numbers, and turn raw data into knowledge.
Required Skills
* Deep ML intuition. You think naturally in embeddings, matrices, tensors, projections, gradients, and loss landscapes—and you can explain those ideas without a whiteboard meltdown.
* End-to-end modeling skill. From data wrangling to metric-driven deployment, you spot promising ML opportunities and turn them into working systems.
* Fluency in Python (and friends). PyTorch, TensorFlow, NumPy, pandas, SQL—you’re productive across the modern ML stack and pick up new tools quickly.
* Data-driven detective work. You’re comfortable sifting through terabytes of noisy data to surface the patterns that matter.
* Research mindset. Reading arXiv before breakfast, reproducing baselines, and adapting fresh papers to real problems feels like fun, not homework.
* Parallel-experiment discipline. You’re organized enough to run and track multiple training jobs at once without losing your mind (or your metrics).
* Clear communicator. You debate model choices and share experimental results—in English—across time zones.
Benefits
* Stay on the cutting edge, diving into both seminal papers and the latest conference breakthroughs.
* Leverage ample compute resources to run ambitious training cycles, scale up ideas quickly, and iterate based on what you learned.
* Take risks and experiment with new approaches, as long as you learn something.
Recruiting Process
* Online technical assessment
* Technical interview
* Cultural interviews