Senior Software Engineer, Machine Learning Platform

What you'll do

• Design reliable data and model infrastructure and productionize machine learning workflows.
• Improve experimentation and risk decisioning speed and quality.
• Partner with ML engineers, data scientists, product managers, and risk specialists to build dependable systems for fraud and risk problems.

What they ask for

• 5+ years of software engineering experience, including at least 3+ years focused on model training infrastructure, model serving systems, or MLOps platforms.
• Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
• Hands-on experience with deep learning frameworks and model training execution engines; strong proficiency in Python, Java, or C++.
• Experience with distributed orchestration and workflow management tools, plus solid understanding of GPUs and GPU-based training or inference optimization.

Nice to have

• Experience with model acceleration frameworks and Large Language Models (LLMs).
• Experience with cloud platforms (e.g., AWS, GCP) and large-scale, low-latency production ML infrastructure; profiling with NVIDIA Nsight Systems is also preferred.

Pay

$140 – $245 (currency and pay basis not stated); Offers Equity; Offers Bonus

Tools

PyTorch, TensorFlow, JAX, Python, Java, C++, Kubernetes, Ray

AI focus

Build and operate machine learning infrastructure for risk and fraud decisioning, including model training, serving, monitoring, and optimization.