Senior Machine Learning Engineer

Are you passionate about shaping the future of healthcare through machine learning? We're looking for a Senior Machine Learning Engineer who thrives at the intersection of cutting-edge ML and large-scale production systems. In this role, you'll be pivotal in designing, deploying, and scaling real-world ML services that drive better clinical and business outcomes.

You'll work alongside data scientists, engineers, and domain experts to build scalable ML solutions using tools like AWS SageMaker, Airflow, GitLab CI, and dbt on Snowflake/Redshift. Your work will span the full ML lifecycle-from feature engineering and training pipelines to inference, monitoring, and continuous improvement.

What We Provide

What You Will Do

• Bridge research and production by deploying ML models that are not just powerful-but reliable, scalable, and maintainable.
• Collaborate with data scientists to experiment with advanced ML/AI algorithms and frameworks.
• Design and implement feature pipelines using DBT, Snowflake, and Redshift to power high-quality ML models.
• Build and optimize production-grade ML pipelines for training, inference, and monitoring using SageMaker, GitLab CI, and Airflow.
• Monitor and manage model performance, ensuring ML systems remain trustworthy and impactful in production.
• Contribute to system architecture and design, making key decisions that influence the reliability and scalability of our ML platform.
• Mentor junior engineers, fostering a culture of innovation, quality, and engineering excellence.
• Identify and resolve runtime and infrastructure issues, ensuring smooth model deployment and system stability.
• Ensure models meet requirements for explainability, auditability, and responsible AI in regulated healthcare settings.

Why Join Us?

• Innovation at scale: Build ML systems that directly impact real-world healthcare outcomes.
• Modern tech stack: Work with cutting-edge tools in cloud-native environments.
• Collaborative culture: Join a team that values curiosity, continuous learning, and shared success.
• Career growth: Lead initiatives, mentor peers, and shape the future of ML engineering here.

Qualifications

Licenses and Certifications:

• AWS certifications (e.g., ML Specialty, Solutions Architect) preferred

Education:

• Bachelor's Degree in Computer Science or a related discipline required
• Master's Degree in Computer Science or a related discipline preferred

Work Experience:

• 3+ years of experience building and deploying production machine learning systems required
• Proficient in Python and key ML libraries required
• Strong experience with containerized ML services (e.g., Kubeflow, SageMaker, MLFlow) and cloud platforms (AWS preferred) required
• Proficient with MLOps tooling and observability stacks required
• Skilled in GitOps, SQL, bash scripting, and CI/CD workflows required
• Effective oral, written and interpersonal communication skills required
• Experience in healthcare AI and ML system design preferred

Licenses and Certifications:

• AWS certifications relevant to ML/AI:
• AWS Certified Cloud Practitioner
• AWS Certified AI practitioner
• AWS Certified Solutions Architect Associate
• AWS Certified Machine Learning Engineer Associate
• AWS Certified Data Engineer
• AWS Certified Machine Learning Specialty

Education:

• Bachelor's Degree in Computer Science or a related discipline required
• Master's Degree in Computer Science or a related discipline preferred

Work Experience:

• 3+ years of experience building and deploying production machine learning systems required
• Proficient in Python and key ML libraries required
• Strong experience with containerized ML services (e.g., Kubeflow, SageMaker, MLFlow) and cloud platforms (AWS preferred) required
• Proficient with MLOps tooling and observability stacks required
• Skilled in GitOps, SQL, bash scripting, and CI/CD workflows required
• Effective oral, written and interpersonal communication skills required
• Experience in healthcare AI and ML system design preferred