Staff Software Engineer – GenAI & Agentic Workflows

Machinify • Palo Alto, CA

1d

About The Position

As a Staff Software Engineer at Machinify, you will lead the design and development of large-scale systems that bring cutting-edge AI into production to transform Payment Integrity and Audit solutions. You will:
Lead development of document processing pipelines capable of annually handling hundreds of millions of documents, including medical records, claims, and benefits data.
Design and implement agentic workflows that review and validate millions of healthcare claims per year, ensuring accuracy and payment integrity.
Evolve our agentic ecosystem by driving innovation in prompt development lifecycle, optimal utilization of various flavors of multi-modal (text + vision) LLMs, context extraction, RAG, workflow orchestration frameworks, observability and logging, and explainability of LLM outputs.
Collaborate across teams (Data Science, Data Engineering, and Product) to deliver robust, end-to-end AI-powered systems that automate complex workflows.
Address critical infrastructure needs by tracking and reducing tech debt while strengthening system reliability and scalability.
Mentor and guide engineers on best practices in designing production-grade AI systems, helping set technical direction for the team.

Requirements

• 6+ years of software engineering experience, including at least 2 years building agentic systems or AI-driven workflows in an enterprise setting.
• Strong programming expertise in Java and Python, with additional experience in Scala a significant plus.
• Experience designing and scaling large-scale, distributed systems and pipelines, ideally processing unstructured data such as documents or healthcare records.
• Proven ability to productionize AI/ML techniques (e.g., LLM prompt engineering, RAG, workflow automation, and agents) with a focus on reliability, observability, and explainability.
• Knowledge of monitoring and observability tools (Prometheus, Grafana or similar) and passion for tracking metrics.
• Strong CS fundamentals, including data structures, asynchronous programming, and system design.
• Demonstrated success in reducing manual processes through automation, ideally in healthcare, fintech, or other regulated domains.
• Collaborative mindset with experience working closely with data scientists and cross-functional teams to translate requirements into technical designs and production systems.
• A passion for pushing the boundaries of AI to deliver better-than-human-level accuracy and efficiency at scale.
• Bachelor’s or Master’s degree in Computer Science or a related field (or equivalent practical experience).
• Track record of building and shipping enterprise-grade AI/ML systems in production.
• Strong testing and code quality discipline, with experience contributing to and improving enterprise-grade systems.

Nice To Haves

• Experience with big-data tools and distributed systems frameworks (e.g., Spark, Flink, Kafka, Airflow, Ray) is a strong plus.

Responsibilities

• Lead development of document processing pipelines capable of annually handling hundreds of millions of documents, including medical records, claims, and benefits data.
• Design and implement agentic workflows that review and validate millions of healthcare claims per year, ensuring accuracy and payment integrity.
• Evolve our agentic ecosystem by driving innovation in prompt development lifecycle, optimal utilization of various flavors of multi-modal (text + vision) LLMs, context extraction, RAG, workflow orchestration frameworks, observability and logging, and explainability of LLM outputs.
• Collaborate across teams (Data Science, Data Engineering, and Product) to deliver robust, end-to-end AI-powered systems that automate complex workflows.
• Address critical infrastructure needs by tracking and reducing tech debt while strengthening system reliability and scalability.
• Mentor and guide engineers on best practices in designing production-grade AI systems, helping set technical direction for the team.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level