Jobs at SAIVA AI
We’re always looking for passionate, mission-driven individuals to help us transform healthcare.
Explore our open positions below and see where you can make an impact.
Machine Learning Engineer
Remote Position
Position Description
Work to explore, design, and modify different algorithms and models that enable machines to learn from data. Advise our deployment team in running your models and reports and placing them in the hands of numerous healthcare professionals across America 365 days a year.
This is a fully remote role with periodic team gatherings, often in Mountain View, California.
- Design, code and test machine learning prototypes from structured and unstructured textual data
- Perform error analysis to derive actionable insights into model improvement
- Guide data engineering and production deployment
- BS in Computer Science, Data Science, Statistics, Econometrics, Physics or Applied Mathematics
- Course based understanding of the foundational concepts of machine learning
- 2+ years of industry experience in machine learning engineering
- Proficiency in Python, PyTorch, and SQL
- Experience with tabular data, time series and/or foundation models based NLP and NLU
- Attention to detail and ability to work independently
- Excellent communication skills and ability to work well in a team
- More advanced degree in the same fields listed above
- 1+ years of guided research work in a university computational lab
- Familiarity with cloud-based infrastructure (AWS, git preferred)
SAIVA AI offers competitive compensation and benefits, including:
- Medical, Dental, Vision Insurance
- 401(k)
- PTO
- Stock Options
Senior MLOps Engineer (Production)
Remote Position (Europe or India)
Position Description
Work to design, build, and deploy machine learning systems that power predictive analytics and intelligent decision support in healthcare. Take ownership of the full ML lifecycle—including data prep, feature engineering, training, evaluation, deployment, and MLOps practices such as monitoring, scaling, and optimizing production systems—to ensure models are robust, scalable, and cost-efficient.
This is a fully remote role, open to candidates based in Europe or India, with periodic team gatherings often in Mountain View, California.
ML Pipelines & Workflows: Build reliable, scalable, and reproducible ML pipelines using Python, PySpark, SQL, and orchestration tools such as Airflow (AWS MWAA).
Model Deployment: Productionize and deploy ML models to solve complex healthcare problems.
MLOps & Automation: Design and manage automated deployment workflows using Docker, ECS/Kubernetes, and CI/CD pipelines.
Collaboration: Partner with research teams to transform prototypes into production-ready products.
Monitoring & On-Call: Implement monitoring, logging, and retraining workflows to maintain model performance and address drift. Actively participate in the on-call process to monitor and troubleshoot ML pipelines.
Cloud Infrastructure: Leverage AWS services (S3, SageMaker, Lambda, Athena, etc.) to efficiently train, deploy, and serve models.
Integration: Connect models with real-time and batch data sources via APIs and data pipelines.
Engineering Excellence: Write clean, modular, well-documented code; contribute to code reviews and system design discussions.
Innovation: Stay current with emerging tools, research trends, and best practices in ML, MLOps/LLMOps, and production-scale AI.
5+ years of experience in applied machine learning and data science, with strong MLOps expertise including building, deploying, and maintaining production ML models and pipelines.
Strong programming skills in Python (Pandas, PySpark, LightGBM, PyTorch, HuggingFace, etc.) and SQL.
Proven experience with model development, feature engineering, evaluation, and production deployment in real-world environments.
Proficiency with ML workflow orchestration tools such as Airflow (required).
Experience with additional orchestration frameworks (e.g., Prefect, Dagster, Kubeflow, Metaflow, Pachyderm, Flyte) is a plus.
Solid understanding of AWS infrastructure for ML (S3, SageMaker, ECS, Step Functions).
Hands-on experience with Docker; Kubernetes/ECS preferred for packaging and deploying models.
Familiarity with CI/CD for ML (e.g., CircleCI, MLflow, model versioning, testing, automation).
Willingness to participate in the on-call rotation to monitor and support production ML pipelines.
Comfortable working in fast-paced, cross-functional teams with a strong product mindset.
Excellent written and verbal communication skills.
- Experience with NLP, time series, or LLM-based models
Senior Data Engineer
Remote Position (Europe or India)
Position Description
Work to design, build, and scale robust data pipelines that power healthcare analytics nationwide. Drive the infrastructure behind our machine learning systems, ensuring pipelines are reliable, observable, and continuously improving in production.
This is a fully remote role, open to candidates based in Europe or India, with periodic team gatherings often in Mountain View, California.
Design, build, and maintain scalable ETL pipelines using Python (Pandas, PySpark) and SQL, orchestrated with Airflow (MWAA).
Develop and maintain the SAIVA Data Lake / Lakehouse on AWS, ensuring high data quality, governance, scalability, and accessibility.
Run and optimize distributed data processing jobs using Spark on AWS EMR and/or EKS.
Implement robust data ingestion frameworks for batch and streaming sources (APIs, databases, files, event streams).
Enforce data validation and quality checks across pipelines to ensure trustworthy analytics and ML readiness.
Monitor and troubleshoot ETL pipelines with CloudWatch, integrating observability tools like Grafana, Prometheus, or Datadog for proactive alerting.
Automate infrastructure provisioning with Terraform, following AWS infrastructure-as-code best practices.
Manage multiple data sources and warehouses, including SQL Server, PostgreSQL, and Snowflake, ensuring efficient integration into the Lakehouse.
Participate in an on-call rotation to support pipeline health, quickly resolve incidents, and minimize downtime.
Write clean, production-grade code and contribute to design/code reviews and engineering best practices.
Experience: 5+ years in data engineering, ETL pipeline development, or data platform roles (flexible for exceptional candidates).
Data Architecture: Proven experience designing and operating data lakes or Lakehouse architectures on AWS (e.g., S3 + Glue + Lake Formation, Delta Lake, or Iceberg).
Databases: Strong SQL skills; hands-on experience with PostgreSQL, SQL Server, and at least one cloud warehouse on AWS (Snowflake or Redshift).
Programming: Proficiency in Python (Pandas, PySpark) for data transformation and ETL; knowledge of Scala or Java is a plus.
Data Processing: Deep hands-on experience with Spark on AWS EMR and/or EKS for distributed data processing.
Orchestration: Strong experience with Airflow (MWAA) for workflow orchestration on AWS.
AWS Cloud Services: Expertise with S3, Glue, Lambda, Athena, Step Functions, ECS, and CloudWatch for building and operating data platforms.
DevOps & Infra: Proficiency with Terraform for infrastructure-as-code on AWS; familiarity with Docker, ECS, and CI/CD pipelines.
Reliability: Experience building monitoring, validation, and alerting into pipelines using CloudWatch and integrations like Grafana, Prometheus, or Datadog.
Collaboration: Strong communication skills with the ability to work effectively with data scientists, analysts, and engineering/product teams.
Mindset: Demonstrated ability to deliver production-ready, scalable AWS data pipelines — not just prototypes or academic projects.
Join
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At SAIVA AI, we are revolutionizing aging care by leveraging machine learning to transform millions of real-world health records, every single day, into life-saving predictions. Our tools empower thousands of frontline health workers to deliver timely, data-driven interventions for the most vulnerable populations.
We are a passionate team of healthcare technology veterans, engineers, and data scientists with deep roots in both the healthcare sector and academia.
Join us. Help make aging with dignity a way of life.