Senior Machine Learning Engineer

nitra • New York, NY

1d • Hybrid

About The Position

Nitra's mission is to build a more efficient healthcare system and the technology that makes it possible. They have built AI products that help doctors better manage their practices, allowing them to focus on patient care. The company is scaling rapidly and is on a clear trajectory toward becoming a unicorn this year, aiming to be a category-defining company. Nitra is seeking a Senior Machine Learning Engineer to architect and build its next-generation data and AI platform, which will power intelligent products across healthcare and fintech. This role is 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 direct contribution to customer-facing agentic systems (e.g., revenue cycle management, care coordination, voice AI). The ideal candidate will be capable of operating across various layers, from data pipelines and model infrastructure to shipping AI products that deliver 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 ($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
• 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

• Experience in healthcare, fintech, or other regulated environments is a plus
• Understanding of data security, compliance, and privacy considerations (e.g., HIPAA, SOC2)

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