Software Engineer - Europe

🌐 Remote, USA ⚡ Future-Ready ✍️ Apply Now

Job Description

DeepInfra is looking for strong Software Engineers to join our team. You’ll work on designing, building, and scaling infrastructure for serving top open-source AI models in production. This role is ideal for engineers who are already comfortable owning problems end-to-end and want to deepen their experience working on high-impact AI systems. If you’re excited about AI/ML and are looking to work on real systems at scale — we’d love to meet you. What You’ll Do Design, develop, and test inference solutions for state-of-the-art AI models Implement, optimize, and evaluate AI models using Python, C++, CUDA, and NCCL Own and operate production model-serving systems, including monitoring and debugging Build new features, improve system performance, and contribute to overall system design Participate in code reviews and technical discussions to maintain high engineering standards Explore and apply new AI/ML techniques to improve model performance and efficiency Take ideas from concept to production What You Bring Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field 3+ years of relevant experience Strong fundamentals in data structures, algorithms, and software design Proficiency in Python and experience working with AI/ML frameworks (e.g., PyTorch, TensorFlow) Hands-on experience building, shipping, and maintaining software systems Familiarity with AI models, Transformers, and Diffusers Experience working with version control (Git) and collaborative development workflows Ability to debug, optimize, and improve existing systems Strong communication skills and ability to work independently in a fast-paced environment Bonus Experience with C++, CUDA, or AI inference Contributions to open-source ML projects Why DeepInfra Work on cutting-edge AI model serving - the systems that power the next generation of LLMs and multimodal models. Small team, huge impact: your work ships directly to customers. Opportunity to learn from engineers building high-performance inference at scale. Fast-paced environment with ownership, autonomy, and end-to-end responsibility.

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