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-timeWashington • District of Columbia, Washington • 🇺🇸 United StatesSeniorWebsite

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 (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.

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 engineeringreinforcement learningMLOpsmodel versioningmodel monitoringdata pipelinesLLMOpsdistributed systemscloud infrastructure

Soft Skills

cross-functional collaborationcommunicationattention to detailownership mindsetmentoringproblem-solvingaction-oriented