Senior Staff ML Ops Engineer
Requisition ID
544590
Category
Research & Development
Location
United States - NY, New York
Location: Hybrid
Medidata follows a hybrid office policy in which employees who are hired for an in-person position are expected to work on site a certain number of days per week in accordance with Company policy.
About our Company:
Medidata is powering smarter treatments and healthier people through digital solutions to support clinical trials. Celebrating 25 years of ground-breaking technological innovation across more than 36,000 trials and 11 million patients, Medidata offers industry-leading expertise, analytics-powered insights, and one of the largest clinical trial data sets in the industry. More than 1 million users trust Medidata’s seamless, end-to-end platform to improve patient experiences, accelerate clinical breakthroughs, and bring therapies to market faster. Discover more at www.medidata.com.
About the Team:
We are seeking a skilled and motivated ML Ops Engineer to join our growing data science and machine learning team. In this role, you will be responsible for building, automating, and maintaining end-to-end machine learning pipelines and infrastructure to support the deployment and operationalization of machine learning models at scale. You will work closely with data scientists, data engineers, and platform teams to ensure our ML models are robust, scalable, and continuously monitored. This role will report to Director Data Science in AI Services.
Responsibilities:
- Design and implement ML pipelines using Apache Airflow to automate data preparation, model training, evaluation, and deployment.
- Support model lifecycle management using tools like MLflow and/or Kubeflow, including tracking experiments, packaging models, and managing deployments.
- Set up and maintain feature engineering platforms, enabling reuse, versioning, and scalability of features across ML workflows.
- Implement model monitoring and drift detection using platforms such as Evidently AI or similar solutions to ensure high model performance and reliability in production.
- Collaborate with data science teams to streamline the transition of models from research to production.
- Ensure systems are scalable, maintainable, and well-documented, following best practices in DevOps, CI/CD, and Infrastructure as Code (IaC).
- Assist in troubleshooting and debugging model and pipeline issues in production environments.
- Advocate for and implement observability, logging, and alerting in ML systems.
- Contribute to the continuous improvement of our ML Ops architecture and strategy.
Qualifications:
- Minimum 8+ years of related experience
- Proven experience with Apache Airflow for orchestrating ML workflows.
- Hands-on experience with MLflow, Kubeflow, or similar platforms for model tracking and orchestration.
- Strong background in building and scaling feature engineering platforms.
- Experience with model monitoring, performance tracking, and drift detection using tools such as Evidently AI, WhyLabs.
- Solid understanding of ML lifecycle management, CI/CD pipelines, and containerization technologies like Docker and Kubernetes.
- Proficient in Python, with knowledge of ML and data engineering libraries (e.g., pandas, scikit-learn, Spark).
- Familiarity with cloud platforms (AWS, GCP, or Azure) and relevant ML/DevOps services.
- Excellent collaboration and communication skills, with the ability to support and mentor data science teams.
- Experience with Terraform or other Infrastructure as Code (IaC) tools.
Preferred Qualifications:
- Knowledge of real-time data processing frameworks and streaming tools.
- Prior experience working in a highly regulated or production-critical environment.
- Familiarity with Large Language Models (LLMs) and a solid understanding of LLMOps practices, including prompt engineering, fine-tuning, and serving LLMs in production.
- Hands-on experience with LLMOps tools, such as:
- LangChain, LlamaIndex, or Haystack for orchestration and retrieval-augmented generation (RAG)
- Weights & Biases, MLflow, or ClearML for experiment tracking and versioning
- Ray Serve, FastAPI, or vLLM for scalable LLM inference
- Prompt Layer or TruLens for prompt versioning, testing, and monitoring
- Experience working with industry-standard feature engineering platforms/tools, such as:
- Feast, Tecton, or Hopsworks for feature store implementations
- Feature tools or PySpark for automated feature engineering and transformation pipelines
- Experience with Terraform or other Infrastructure as Code (IaC) tools for managing ML infrastructure.
- Understanding of data processing and streaming frameworks like Apache Kafka, Apache Flink, or Spark Streaming.
The salary range posted below refers only to positions that will be physically based in New York City Metro Area. As with all roles, Medidata sets ranges based on many factors including function, level, candidate experience, and geographic location. Pay ranges for candidates in locations other than New York City Metro Area, may differ based on the local market data in that region.
The base salary pay range for this position physically located in New York/ New Jersey is $157,500-$210,000.
The base salary pay range for this position physically located in California is $166,500-$222,000.
The base salary pay range for this position physically located in Boston is $155,250-$207,000.
The base salary pay range for this position physically located in Ohio is $138,750-$185,000.
The base salary pay range for this position National remote is $141,000-$188,000.
Base pay is one part of the Total Rewards that Medidata provides to compensate and recognize employees for their work. Most sales positions are eligible for a commission on the terms of applicable plan documents, and many of Medidata's non-sales positions are eligible for annual bonuses. Medidata believes that benefits should connect you to the support you need when it matters most and provides best-in-class benefits, including medical, dental, life and disability insurance; 401(k) matching; flexible paid time off; and 10 paid holidays per year.
Equal Employment Opportunity:
In order to provide equal employment and advancement opportunities to all individuals, employment decisions at Medidata are based on merit, qualifications and abilities. Medidata is committed to a policy of non-discrimination and equal opportunity for all employees and qualified applicants without regard to race, color, religion, gender, sex (including pregnancy, childbirth or medical or common conditions related to pregnancy or childbirth), sexual orientation, gender identity, gender expression, marital status, familial status, national origin, ancestry, age, disability, veteran status, military service, application for military service, genetic information, receipt of free medical care, or any other characteristic protected under applicable law. Medidata will make reasonable accommodations for qualified individuals with known disabilities, in accordance with applicable law.
Applications will be accepted on an ongoing basis until the position is filled.
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