Senior Machine Learning Engineer

Data + AI Platform

• 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

Applied Machine Learning

• 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 

Product Collaboration

• 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

Engineering Excellence

• 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

Core Experience

• 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)

MLOps / LLMOps

• 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)

Data Platform

• 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

Domain

• Experience in healthcare, fintech, or other regulated environments is a plus

• Understanding of data security, compliance, and privacy considerations (e.g., HIPAA, SOC2)

Culture

• 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

• 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.