# Senior Machine Learning Engineer, Multimodal AI

**Company:** [Hike Medical](https://hotfix.jobs/companies/hike-medical)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Salary:** $170k – $300k/yr
**Experience:** 5+ years
**Skills:** Python, LLMs, Ocr, AWS, Document Intelligence, Voice Ai, Prompt Engineering, Model Evaluation, Serverless, Structured Outputs
**Posted:** 2026-06-23

> Build and productionize multimodal AI systems that turn unstructured healthcare documents, faxes, and transcripts into reliable structured data and automated workflows using LLMs, OCR, and voice AI.

## Job Description

## 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.

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