Senior Machine Learning Engineer

Role Overview

• Design and build scalable ML/AI infrastructure, including feature stores, model serving, data streaming, evaluation frameworks, and observability systems
• Build and maintain data pipelines for structured and unstructured data (claims, EHR, transactions, logs)
• Ensure data quality, lineage, and reliability across the platform
• Ensure compliance and security for data handling, including adherence to healthcare and financial data standards
• Empower teams to access data and turn into actionable insights with agentic analytics
• Prototype and productionize ML models for:
• Anomaly detection (e.g., billing irregularities, operational outliers)
• Predictive modeling (e.g., claims risk, fraud)
• Build and deploy models across use cases like:
• Revenue cycle management (automated coding, denial management, prior auth)
• Care coordination (clinical reasoning, workflow automation)
• Establish and own best practices across MLOps and LLMOps, including:
• Model lifecycle management (training, versioning, deployment, monitoring)
• LLM evaluation, prompt/version control, and experimentation frameworks
• CI/CD for ML systems and reproducible pipelines
• Develop systems for LLM orchestration and agent frameworks (tool use, memory, retrieval, multi-step reasoning)
• Understand drivers and implement solutions for agent performance, e.g. model selection, memory, context windows prompt engineering, agent orchestration, fine-tuning
• Partner closely with forward-deployed Product, Data Science, and GTM teams to translate ambiguous problems into production-ready AI systems
• Own end-to-end delivery, from experimentation to deployment and iteration
• Contribute to defining Nitra’s agentic AI product strategy
• Establish best practices for model evaluation, monitoring, and safety
• Improve system reliability, latency, and cost efficiency at scale
• Mentor engineers and help raise the bar for ML across the team.

Requirements

• 4+ years of experience in machine learning and data engineering
• Strong background in ML frameworks for reinforcement learning
• Hands-on experience with multi-agent systems, evaluation, and observability
• Proven experience deploying ML systems into production at scale (think: $billions in volume)
• Hands-on experience with MLOps practices, including:
• Model versioning, monitoring, and retraining pipelines
• Experiment tracking and reproducibility
• Experience with LLMOps tooling and workflows, including:
• Prompt management and evaluation
• RAG systems and vector databases
• LLM performance optimization (latency, cost, quality)
• Experience building data pipelines (batch + streaming) and working with large-scale datasets
• Strong understanding of distributed systems and cloud infrastructure (AWS/GCP/Azure)
• Familiarity with tools like Airflow, Spark, dbt, or similar
• Experience in healthcare, fintech, or other regulated environments is a plus
• Understanding of data security, compliance, and privacy considerations (e.g., HIPAA, SOC2)
• Ability to work cross-functionally and communicate complex ideas clearly
• Experience working closely with product and business stakeholders
• High attention to detail with a bias toward action
• Strong ownership mindset—you don’t just build models, you solve problems end-to-end.

Tech Stack

• Airflow
• AWS
• Azure
• Cloud
• Distributed Systems
• Google Cloud Platform
• Spark

Benefits

• Equity
• Everyone at Nitra is an owner. When the company wins, you win.
• Competitive Salary
• You’re the best of the best, and your salary will reflect your experience and reward your contributions to Nitra.
• Health Care
• Your health comes first. We offer comprehensive health, vision, and dental insurance options.
• Retirement Benefits
• Your financial stability matters to us so we provide a generous employer 401K match.
• Hybrid Policy
• Nitra maintains a hybrid work policy, with team members working from the office four days per week and Wednesdays designated as a work-from-home day.