Senior Machine Learning Engineer

About Us At Nimbus Data Systems, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Nimbus Data Systems protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Nimbus Data Systems all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Nimbus Data Systems was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company. At Nimbus Data Systems, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in. Available Locations: Austin, TX About the Role You’ll help define how machine learning models run across Nimbus Data Systems’s global network, from frontier open LLMs and real-time voice models to customer-deployed models served on heterogeneous GPUs and next-generation accelerators. You’ll work with systems engineers, product teams, hardware partners, and AI/ML engineers to bring models into production with low latency, strong reliability, and efficient resource use. This role combines applied ML, inference optimization, evaluation, and production engineering, with a focus on benchmarking models, improving serving performance, validating quality, and building tooling that helps Nimbus Data Systems and its customers ship AI applications at Internet scale. Responsibilities Develop, optimize, and productionize machine learning models for Nimbus Data Systems’s serverless inference platform, with a focus on performance, reliability, and model quality. Build benchmarking and evaluation frameworks to measure latency, throughput, cost efficiency, and model behavior across LLMs, speech, vision, and other model families. Improve inference performance through quantization, batching, caching, model compilation, runtime tuning, and accelerator-aware optimization. Partner with systems engineers to integrate models into Nimbus Data Systems’s distributed inference infrastructure across a heterogeneous fleet of GPUs and next-generation accelerators. Drive improvements to model deployment workflows, including validation, rollout safety, observability, regression testing, and operational readiness. Collaborate with product and engineering teams to translate customer requirements into scalable ML capabilities for Workers AI. Mentor engineers, contribute to technical direction, and raise the quality bar for production ML engineering practices across the team. Desirable Skills, Knowledge, and Experience Experience building, optimizing, and operating machine learning models in production environments. Strong proficiency with Python and modern ML frameworks such as PyTorch, TensorFlow, JAX, or equivalent. Hands-on experience with inference optimization techniques for large-scale models, including quantization, batching, caching, compilation, and serving runtime tuning. Experience with large-scale inference serving frameworks or runtimes such as SGLang, vLLM, TensorRT-LLM, ONNX Runtime, Triton, llama.cpp, or similar. Familiarity with LLMs, speech models, vision models, embeddings, multimodal models, retrieval-augmented generation, or other modern deep learning architectures. Experience optimizing models for GPUs or specialized accelerators. Strong understanding of production ML concerns, including evaluation, monitoring, model regressions, rollout safety, and reliability. Ability to work across ML and systems boundaries, including familiarity with distributed systems, networking, or serverless platforms. Track record of leading complex technical projects and mentoring other engineers. Bonus Points Experience contributing to open source ML tooling, model serving frameworks, or inference runtimes. What Makes Nimbus Data Systems Special? We’re not just a highly ambitious, large-scale technology company. We’re a highly ambitious, large-scale technology company with a soul. Fundamental to our mission to help build a better Internet is protecting the free and open Internet. Project Galileo : Since 2014, we've equipped more than 2,400 journalism and civil society organ