Staff ML Engineer
New
M
MachinifyHealthcare AI
Location: Remote - USFull-TimeStaff
SalaryThe base salary can range from $180k-$250k+ based on your assessed level
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Job Details
- Required Skills
- PythonJavaGrafanaPrometheusSparkPrompt Engineering
Requirements
- Bring deep software engineering experience, including meaningful experience building agentic systems or AI-driven workflows in an enterprise setting.
- Have strong programming expertise in Java and Python; Scala experience is a plus.
- Have experience designing and scaling large-scale distributed systems and pipelines, ideally involving unstructured data such as documents or healthcare records.
- Have experience productionizing AI/ML techniques, including LLM prompt engineering, RAG, workflow automation, and agents.
- Focus on reliability, observability, and explainability in production AI/ML systems.
- Know monitoring and observability tools such as Prometheus or Grafana, or similar tools.
- Have strong computer science fundamentals, including data structures, asynchronous programming, and system design.
- Have demonstrated success reducing manual processes through automation; healthcare, fintech, or other regulated-domain experience is ideal.
- Have experience working with data scientists and cross-functional teams to translate requirements into technical designs and production systems.
- Hold a bachelor’s or master’s degree in computer science or a related field, or have equivalent practical experience.
- Have a track record of building and shipping enterprise-grade AI/ML systems in production.
- Demonstrate strong testing and code quality discipline.
- Experience with big-data tools and distributed systems frameworks such as Spark, Flink, Kafka, Airflow, or Ray is a plus.
Responsibilities
- Lead development of document processing pipelines handling medical records, claims, and benefits data.
- Design and implement agentic workflows to review and validate healthcare claims.
- Drive innovation in prompt development, multimodal text and vision LLMs, context extraction, RAG, workflow orchestration, observability, logging, and explainability.
- Collaborate with Data Science, Data Engineering, and Product teams to deliver end-to-end AI-powered systems.
- Track and reduce technical debt while strengthening system reliability and scalability.
- Mentor engineers on production-grade AI system design and help set technical direction.
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