Senior Machine Learning Engineer, Multimodal AI

What You’ll Work On

• Build and improve multimodal AI pipelines that process healthcare documents, OCR output, transcripts, and workflow context into structured facts and decisions.
• Design LLM-powered extraction, classification, validation, and routing systems for operational and clinical workflows.
• Improve document intelligence systems across OCR, schema extraction, confidence scoring, error handling, and low-quality input recovery.
• Develop voice AI workflows for patient and provider outreach, transcript understanding, post-call extraction, and follow-up automation.
• Create evaluation harnesses, benchmarks, and regression tests for extraction quality, hallucination prevention, workflow accuracy, and model changes.
• Decide when to use LLMs, deterministic logic, retrieval, human review, or hybrid systems to maximize quality and reliability.
• Partner with product and engineering to identify the highest-leverage automation opportunities and translate them into shipped systems.
• Optimize cost, latency, and reliability across model providers and infrastructure layers.
• Work closely with backend engineers to deploy AI systems into our AWS and serverless environment with strong observability and operational rigor.

Technical Requirements

• Strong experience building production AI systems around LLMs, OCR, and unstructured data workflows.
• Proven track record shipping applied AI products, not just prototyping models offline.
• Deep familiarity with modern LLM workflows including prompting, structured outputs, tool use, retries, fallbacks, guardrails, and model evaluation.
• Experience with document intelligence systems such as OCR pipelines, document extraction, classification, post-processing, and confidence-based review flows.
• Experience with voice or conversational AI, or adjacent systems involving transcripts, call automation, and conversational extraction.
• Strong proficiency in Python and comfort working in production codebases with APIs, queues, and backend services.
• Experience deploying and operating AI systems in AWS or similar cloud environments, including serverless or event-driven architectures.
• Strong instincts around evaluation, benchmarking, monitoring, and quality assurance for real-world AI systems.
• Ability to work across structured and unstructured data and design systems that are robust to noisy, incomplete, and ambiguous inputs.

Nice to Have

• Experience in healthcare, claims, revenue cycle, or regulated operational environments.
• Experience with human-in-the-loop workflow design and review tooling.
• Familiarity with telephony vendors, speech systems, or conversational agent infrastructure.
• Experience comparing and routing across model providers such as OpenAI, Anthropic, Bedrock, or equivalent.
• Experience designing internal tools or operational systems used directly by workflow teams.
• Background in machine learning, applied NLP, information extraction, or related fields.