About Payoneer Founded in 2005, Payoneer is the global financial platform that removes friction from doing business across borders, with a mission to connect the world’s underserved businesses to a rising global economy. We’re a community with over 2,500 colleagues all over the world, working to serve customers, and partners in over 190 countries and territories
By taking the complexity out of the financial workflows–including everything from global payments and compliance to multi-currency and workforce management, to providing working capital and business intelligence–we give businesses the tools they need to work efficiently worldwide and grow with confidence
Role Summary We are looking for a technically strong AI / ML Developer with hands-on expertise in training and fine-tuning Small Language Models (SLMs) and RAG Solutions. The ideal candidate will drive end-to-end AI Solutions — from dataset curation and pre-processing to training, evaluation, and production deployment. You will collaborate closely with product, product engineers, and infrastructure teams to build AI solutions that are efficient, scalable, and business-aligned
Key Responsibilities 1 Model Design & Training Design, train, and fine-tune Small Language Models (SLMs) using frameworks such as PyTorch, TensorFlow, or JAX
Conduct experiments with supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and instruction tuning
Implement efficient training pipelines leveraging distributed training (DDP, FSDP) across GPU/TPU clusters
Perform hyperparameter optimisation, ablation studies, and model selection based on benchmark results
Develop and maintain data pipelines for collecting, cleaning, tokenising, and pre-processing large-scale training corpora
2 Model Evaluation & Quality Define and implement evaluation frameworks including perplexity, BLEU, ROUGE, BERTScore, and task-specific benchmarks
Conduct red-teaming, bias analysis, and safety evaluations to ensure responsible AI deployment
Benchmark models against established baselines (e.g., GPT-2, Phi, Mistral) and track performance over iterations
Collaborate with QA teams to build regression suites for model versioning and continuous evaluation
3 MLOps & Deployment Containerise and deploy models using Docker, Kubernetes, and cloud-native ML platforms (AWS SageMaker / GCP Vertex AI / Azure ML)
Build and maintain model registries, experiment tracking (MLflow, Comet), and reproducible training pipelines
Optimise inference performance through quantisation (INT8, INT4), pruning, distillation, and ONNX/TensorRT conversion
Monitor model drift, data drift,