Senior Machine Learning Engineer

Nitra

Salary: $228k - $344k
Experience: 4+ yrs
Location: New York, New York, United States
Job type: Full-time

Required skills

ML frameworks
MLOps
LLMOps
Python
Spark
Airflow
dbt
cloud platforms (AWS/GCP/Azure)
vector databases

About the role

Who we are: Nitra's mission is to build a more efficient healthcare system and the technology that makes it possible. We built AI products that help doctors better manage their practices, so they can have time back to focus on what matters to them most.

We are scaling rapidly and on a clear trajectory toward becoming a unicorn this year. We are building a category-defining company, and we’re looking for people who want to do the most meaningful work of their careers.

If you want comfort, this isn’t the place. We operate with urgency, intensity, and ambition.
If you want to take ownership in building a generational company, come join us.

Nitra was created by unicorn founders, and joined by an experienced team from Microsoft, Meta, Plaid, PayPal, BCG, Morgan Stanley, and more. The team is backed by some of the world’s leading VCs (Andreessen Horowitz, NEA, etc.) and is supported by an expert group of advisors including the co-founders of Square, former Governors, and co-founder of CityMD.

We're looking 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 role sits at the intersection of applied AI and platform engineering. You will design and deploy systems that enable internal agentic workflows (ex: GTM, product intelligence), while also contributing directly to customer-facing agentic systems (ex: revenue cycle management, care coordination, voice AI).

We need someone who can operate across layers—from data pipelines and model infrastructure to shipping AI products that drive real-world outcomes for providers.

Your responsibilities will include:

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

You have:

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)

We offer:

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.

The total compensation range for this full-time position is $228,960–$344,160, which includes base salary, bonus, equity, and benefits. Our salary ranges are determined by role, level, and location. The range displayed reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Please note that the compensation details listed reflect the base salary only, and do not include bonus, equity, or benefits.

Nitra values diversity. We are committed to equal opportunities and creating an inclusive environment for all our employees. We welcome applicants regardless of ethnicity, national origin or ancestry, gender, race, religious beliefs, disability, sex, sexual orientation, age, veteran status, genetic information, citizenship, or any other characteristic protected by law.

About Nitra

AI-native business operating system for medical practices.

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