Nitra

Customer Success Manager

• Managing the day-to-day relationships with private practice owners and admins to ensure high product adoption and satisfaction.
• Build experiments to deliver on retention and expansion metrics such as churn reduction and feature upsells.
• Using surveys, customer interviews, and deep research to determine the best communication strategy for non-tech-savvy users.
• Developing the customer communication strategy for the product to simplify complex fintech concepts for medical staff.
• Working closely with stakeholders across the company to further hone messaging based on direct customer feedback.

• Leading and mentoring a team of CSMs to drive product adoption and long-term customer satisfaction.
• Managing our multi-channel post-sale engagement presence to ensure a strong and healthy customer base.
• Build experiments to deliver on Success metrics such as retention, churn reduction, and Net Promoter Score.
• Using surveys, customer interviews, and deep research to determine the most effective engagement strategies.
• Developing the customer communication strategy for the product to make fintech workflows intuitive and accessible for all practice staff.
• Working closely with stakeholders across the company to further hone messaging and ensure the voice of the customer is represented in our product roadmap.

Customer Success Lead

Content Marketing Manager

• Managing our multi-channel paid and organic campaign presence to build a strong top of funnel
• Build experiments to deliver on Marketing metrics (ROI, open rates, etc.)
• Using surveys, customer interviews, and deep research to determine channel strategy
• Developing the customer communication strategy for the product
• Working closely with stakeholders across the company to further hone messaging

Machine Learning Engineer

• 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.

Senior Machine Learning Engineer

• 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 and predictive modeling.
• Build and deploy models across use cases like revenue cycle management and clinical reasoning.
• Establish and own best practices across MLOps and LLMOps, including model lifecycle management and CI/CD for ML systems.
• Develop systems for LLM orchestration and agent frameworks.
• 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.