Senior Machine Learning Engineer

nitra • Washington, DC

12h • Hybrid

About The Position

Nitra's mission is to build a more efficient healthcare system and the technology that makes it possible. They built AI products that help doctors better manage their practices, aiming to give them time back to focus on what matters most. Nitra is scaling rapidly and on a clear trajectory toward becoming a unicorn this year, building a category-defining company. They operate with urgency, intensity, and ambition, seeking individuals who want to take ownership in building a generational company. Nitra was created by unicorn founders and joined by an experienced team from Microsoft, Meta, Plaid, PayPal, BCG, Morgan Stanley, and more, backed by leading VCs (Andreessen Horowitz, NEA, etc.) and supported by expert advisors. The role is for a Senior Machine Learning Engineer to architect and build Nitra’s next-generation data and AI platform, powering intelligent products across healthcare and fintech. This position sits at the intersection of applied AI and platform engineering, involving the design and deployment of systems for internal agentic workflows (e.g., GTM, product intelligence) and contributing directly to customer-facing agentic systems (e.g., revenue cycle management, care coordination, voice AI). The engineer will operate across layers, from data pipelines and model infrastructure to shipping AI products that drive real-world outcomes for providers.

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
• Hands-on experience with MLOps practices, including: Experiment tracking and reproducibility
• Experience with LLMOps tooling and workflows, including: Prompt management and evaluation
• Experience with LLMOps tooling and workflows, including: RAG systems and vector databases
• Experience with LLMOps tooling and workflows, including: 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
• 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

Nice To Haves

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

Responsibilities

• 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)
• Prototype and productionize ML models for: Predictive modeling (e.g., claims risk, fraud)
• Build and deploy models across use cases like: Revenue cycle management (automated coding, denial management, prior auth)
• Build and deploy models across use cases like: Care coordination (clinical reasoning, workflow automation)
• Establish and own best practices across MLOps and LLMOps, including: Model lifecycle management (training, versioning, deployment, monitoring)
• Establish and own best practices across MLOps and LLMOps, including: LLM evaluation, prompt/version control, and experimentation frameworks
• Establish and own best practices across MLOps and LLMOps, including: 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

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed