Design, implement, and deploy ML-powered software components within a microservice architecture.
Lead development and productionization of NLP and transformer-based models for biomedical information processing.
Own the full ML lifecycle: data preparation, model training, evaluation, optimization, deployment, and inference at scale.
Architect scalable and maintainable ML infrastructure and services.
Collaborate cross-functionally with product, UX, and clinical stakeholders to understand requirements and rapidly prototype new capabilities.
Analyze model and system performance; communicate findings and trade-offs clearly to technical and non-technical stakeholders.
Ensure reliability, scalability, and security of ML services in production environments.
Mentor junior engineers and contribute to raising the technical bar across the team.
Contribute to architectural discussions and strategic technical decisions.
Champion engineering best practices including testing, CI/CD, version control, and documentation.

5+ years of industry experience in machine learning-focused software engineering (excluding internships and academic projects).
Bachelor’s or graduate degree in Computer Science, Mathematics, Electrical Engineering, or a related technical field.
Strong hands-on experience training, testing, deploying, and serving ML models in production environments.
Experience with transformer architectures and NLP applications.
Experience with multi-GPU and multi-node distributed training and inference.
Proficiency in Python.
Proficiency in Java (and/or Kotlin/Scala) or C++.
Experience designing and implementing microservices.
Professional experience with Git and collaborative development workflows.
Experience with relational databases and/or NoSQL systems (e.g., knowledge graphs).
Strong communication skills and ability to explain technical concepts clearly.

Senior Machine Learning Engineer

Key skills