Principal Software Engineer, ML & Distributed Systems

What you'll do

• Design, build, and operationalize scalable machine learning and deep learning models using containers and orchestration platforms.
• Develop and refine LLM prompt engineering and fine-tuning strategies while building evaluation pipelines to optimize quality, latency, and cost.
• Architect and operate multi-tiered distributed services at hyperscale with high availability and fault tolerance.
• Build model serving and inference infrastructure including caching, batching, GPU capacity management, and A/B experimentation.
• Design scalable APIs, data pipelines, and feature stores to ensure reliable data flow between ML systems and products.
• Drive live-site excellence through instrumentation, monitoring, capacity planning, and incident response for ML-backed services.
• Mentor engineers across both machine learning and systems disciplines while participating in architectural discussions.

What we're looking for

• Bachelor's degree in Computer Science or a related technical field is required.
• Candidates must have 6+ years of technical engineering experience with coding in languages like C, C++, C#, Java, JavaScript, or Python.
• A Master's degree and 8+ years of experience, or a Bachelor's degree and 12+ years of experience, are preferred qualifications.
• Experience is required in designing, developing, and operating multi-tiered distributed services at scale.
• Candidates must have hands-on experience building, deploying, and operating machine learning systems in production.
• Proficiency in LLM application patterns including prompt engineering, RAG, fine-tuning, and evaluation frameworks is preferred.
• Experience with ML infrastructure such as distributed training, inference optimization, GPU management, and Kubernetes is preferred.
• Experience with large-scale data systems including streaming, caching (e.g., Redis), feature stores, and experimentation platforms is preferred.