Staff Software Engineer-ML

Job description

Hello there! We're Infrrd. Haven't heard of us before? No problem (it's pronounced In-fur-d ). We are Infrrd, an Enterprise AI company helping global enterprises extract data from complex, unstructured documents like invoices, contracts, engineering drawings, and handwritten notes. Our AI goes beyond data extraction to automate end-to-end workflows, reducing manual effort, improving accuracy, and accelerating business operations. About the Role We are looking for a highly skilled and experienced Staff Software Engineer- ML to lead the design and development of our AI and machine learning systems that power intelligent document extraction and processing. The ideal candidate will drive the Machine Learning architecture decisions, champion engineering best practices, and mentor ML teams while working closely with product, engineering, and business stakeholders. This role sits at the intersection of cutting-edge AI and real-world production systems — you'll shape how we apply NER, classification, LLMs, and extraction models to solve hard, document-heavy problems at scale. Key Responsibilities Own the ML architecture for document extraction, classification, NER, and LLM-powered pipelines. Design scalable, production-ready ML systems with high accuracy, performance, and reliability. Translate business requirements into robust ML solutions and lead architectural decisions. Establish ML engineering standards, evaluation frameworks, and code/model review practices. Collaborate with Product, Engineering, DevOps, and QA to integrate ML capabilities into production. Drive LLM optimization through prompt engineering, RAG, fine-tuning, context management, and inference optimization. Design feedback and correction-tracking systems for continuous model improvement. Identify and mitigate challenges such as hallucinations, model drift, data quality, and scalability. Evaluate emerging AI/GenAI technologies and mentor senior ML engineers and data scientists. Required skills: Strong expertise in NLP, including NER, text classification, sequence labeling, and information extraction. Hands-on experience fine-tuning and deploying LLMs in production. Deep understanding of RAG, prompt engineering, context management, chunking, and inference optimization. Experience building feedback loops and correction-tracking systems. Strong Python skills with PyTorch, Hugging Face, spaCy, LangChain/LlamaIndex. Experience with MLOps tools (MLflow, W&B, model registries, automated retraining, A/B testing). Knowledge of Document AI/OCR models (LayoutLM, Donut, PaddleOCR, or similar). Experience with vector databases (Pinecone, Weaviate, pgvector) and semantic search. Familiarity with model serving (ONNX, TorchServe, Triton, vLLM), cloud platforms (AWS/Azure/GCP), Docker, Kubernetes, and CI/CD. Strong awareness of the evolving AI and GenAI ecosystem. Required qualifications: 10+ years of experience in Machine Learning and AI. 3–5 years in architecture or technical leadership roles. Experience in product-based or SaaS companies preferred. Bachelor's or Master's degree in Computer Science, AI/ML, or a related field. What We're Looking For: Strong analytical and problem-solving skills. Excellent communication and stakeholder management. Proven ability to influence technical direction and mentor engineering teams. Ownership mindset and ability to thrive in a fast-paced environment. Nice to Have: Experience with Agentic AI and AI agents. Background in Document AI, OCR, or Intelligent Document Processing. Experience building high-volume, low-latency ML systems. Knowledge of Responsible AI practices. Experience in document-heavy industries such as fintech, insurance, or healthcare. If you enjoy solving hard engineering problems and building systems that make a real impact, we'd love to meet you. Curious about the kind of engineering challenges we solve? Check out a few of our tech talks: Automated Data Extraction from Engineering & Construction Drawings Ally | Agentic AI for Mortgage NLP-powered Table Extraction for Insurance Policy Data Pay Stubs Data Extraction