Senior Machine Learning Engineer

Nitra

. Design and build scalable ML/AI infrastructure, including feature stores, model serving, data streaming, evaluation frameworks, and observability systems.

Posted 4/22/2026full-timeNew York City • New York • 🇺🇸 United StatesSenior💰 $228,960 - $344,160 per yearWebsite

Tech Stack

Tools & technologies

AirflowAWSAzureCloudDistributed SystemsGoogle Cloud PlatformSpark

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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

Benefits

Comp & perks

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.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningdata engineeringML frameworksreinforcement learningMLOpsmodel versioningmonitoring pipelinesLLMOpsdata pipelinesdistributed systems

Soft Skills

cross-functional collaborationcommunicationattention to detailownership mindsetproblem-solvingaction-oriented