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Staff Machine Learning Engineer

United States - Remote

Tebra only initiates contact with candidates via email from an official Tebra email address (@tebra.com, @patientpop.com, or @kareo.com) or through our applicant tracking system, Greenhouse. We will only ask you to provide sensitive personal information through our official application portal — not via social media or text message. We do not conduct interviews via instant messaging.

About the Role

As a Staff Machine Learning Engineer, you’ll design, train, and operate best-in-class machine learning systems that power our Tebra platform. You’ll own the entire lifecycle — from data exploration and model development to production deployment, monitoring, and continuous improvement.

This is a hands-on technical leadership role where you’ll push the boundaries of applied ML in healthcare, transforming messy real-world data into reliable automation that drives measurable business impact.

Your Area of Focus

  • Design, build, and operate scalable ML pipelines for data ingestion, feature generation, model training, evaluation, deployment, and monitoring.
  • Own the end-to-end ML lifecycle, including data exploration, feature engineering, model design, validation, and productionization.
  • Continuously monitor model performance in production, detect drift, and implement automated retraining pipelines to ensure accuracy and reliability over time.
  • Leverage advanced ML techniques — from gradient boosting to large language models — to improve automation and prediction across claims, payments, and billing workflows.
  • Conduct in-depth data analysis and experimentation to identify new opportunities for model-driven efficiency.
  • Collaborate cross-functionally with engineering, product, and data teams to integrate AI capabilities directly into Tebra’s platform.
  • Establish best practices for model governance, reproducibility, explainability, and observability within regulated healthcare environments.
  • Lead and mentor engineers in applied ML methods, system design, and data-driven experimentation.

Your Professional Qualifications

  • 8+ years of professional software engineering experience, including system design, large-scale services, and production-grade infrastructure.
  • 5+ years of hands-on experience in machine learning engineering or applied AI, with a strong record of deploying and maintaining models in production.
  • Demonstrated ability to deliver significant, measurable real-world impact through applied ML — improving efficiency, automation, or business outcomes.
  • Proficiency in Python, TensorFlow/PyTorch, and scikit-learn.
  • Hands-on experience with data analysis, feature engineering, and model development on large, complex datasets.
  • Strong background in MLOps and data infrastructure (e.g., Airflow, Spark, feature stores, MLflow, data versioning).
  • Proven ability to deploy and maintain ML models in production with CI/CD, monitoring, and alerting.
  • Familiarity with cloud ML environments (AWS, GCP, or Azure) and containerization (Kubernetes, Docker).
  • Experience building or fine-tuning LLMs or generative models for structured business processes.
  • Experience with retrieval-augmented pipelines or feedback-driven model retraining.
  • Experience working with structured business or healthcare data is a plus.
  • Excellent technical communication and a product mindset — comfortable driving initiatives from concept to delivery.

Bonus Points

  • Background in healthcare software operations, or financial automation.
  • Contributions to open-source ML infrastructure projects.
  • Published research or conference papers in machine learning, natural language processing, or applied AI.
  • Experience leading AI reliability and observability initiatives — designing monitoring frameworks, drift detection, and alerting systems for multiple production models.

About Tebra

Kareo and PatientPop have joined forces to become Tebra, the digital backbone for practice well-being. While our teams are still supporting both products, our new hires and current employees are now united as Team Tebra. 

Tebra aims to unlock better healthcare by helping independent practices bring modernized care to patients everywhere. Well over 100,000 providers trust Tebra to elevate their patient experience, and help them grow their practice. At Tebra, we’re building the future of well-being together. That shared vision for tomorrow begins with compassion and humanity today.

Our Values

Start with the Customer 

We get to know our customers - and their patients - and look at the world through their lens.

Keep It Simple

Healthcare is too complex. We aim to simplify it for everyone.

Stay Entrepreneurial 

We reject the status quo and solve problems with creativity, perseverance, and a bias to action.

Better Together

We are diverse, humble, and collaborative. We put the team first and win together.

Celebrate Success

Life is short and joy is underrated. We take time to have fun and celebrate success.

Perks & Benefits 

In addition to our healthcare benefits, we also offer amazing perks! Need work from home basics? We offer a discount through Dell! We also offer a number of resources to help you keep your mind and body healthy. Check out Gympass for a great workout, or TelusEmployee Assistance Program to find mental health resources, along with other resources for everyday occurrences.

#LI-SS1 #LI-Remote

In compliance with California's pay transparency laws, the compensation range for this position will be provided and may include an hourly rate, annual salary, or On-Target Earnings (OTE), depending on the nature of the role. The specific compensation structure and detailed range will be discussed with qualified candidates during the initial talent screen.

Remote Pay Range

$200,000 - $227,700 USD

Tebra is an equal opportunity employer. All applicants will be considered for employment without attention to age, race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

California residents who apply or are recruited for a job with us: please carefully review our California-specific Privacy Notice under the California Consumer Protection Act here: https://www.tebra.com/privacy-policy/california-supplemental-notice/

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Which of the following MLOps components are you comfortable designing, deploying, and maintaining in a production environment? (Select all that apply.) *

1 = CI/CD pipelines for model deployment.
2 = Automated monitoring and alerting for model performance/drift.
3 = Experience with distributed computing/orchestration (e.g., Spark, Airflow).
4 = Feature stores and data versioning systems (e.g., MLflow).
5 = None of the above.

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1 = Extensive experience building or fine-tuning LLMs for structured business processes.
2 = Experience with Retrieval-Augmented Generation (RAG) or feedback-driven model retraining.
3 = Some academic or personal project experience, but not professionally deployed.
4 = No experience with LLMs or Generative AI.

Which data orchestration or processing tools have you used to design, deploy, and manage production-grade feature pipelines for ML systems? (Select all that apply.) *

1 = Apache Airflow (for scheduling/workflow management)
2 = Apache Spark or Dask (for large-scale distributed processing)
3 = Kafka or Kinesis (for streaming data ingestion)
4 = Feature Stores (e.g., Feast, Tecton)
5 = None of the above/Limited experience with these tools.

Which of the following technologies have you used to containerize, deploy, and manage ML model serving (inference) in a production environment? (Select all that apply.) *

1 = Docker (for container image creation)
2 = Kubernetes or ECS/EKS (for orchestration and scaling)
3 = Cloud ML Services (e.g., SageMaker, Vertex AI Endpoints)
4 = Model Registry tools (e.g., MLflow, Sagemaker Model Registry)
5 = None of the above/Limited experience with these deployment tools

Which modern AI methods or tools have you professionally implemented to enhance model performance or structured output reliability? (Select all that apply.) *

1 = LLM Fine-tuning (LoRA, QLoRA, full instruction tuning)
2 = Retrieval-Augmented Generation (RAG) pipelines
3 = Constrained Decoding or Function Calling (to force JSON output)
4 = Transformer Models (e.g., BERT, specialized encoders)
5 = None of the above/Limited experience with these advanced methods

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