Senior Machine Learning (ML) Engineer

Dyania Health•Jersey City, NJ

5h•Remote

About The Position

Dyania Health is a venture-backed company founded in 2019 which has developed Synapsis AI, an end-to-end system that combines a medically post-trained LLM with a physician-driven algorithmic reasoning engine to understand and assess clinical characteristics in electronic medical records. Synapsis AI is designed for installation within the healthcare system's computing environment or on healthcare system private clouds to automate manual chart review of EMRs, without removing any data from the healthcare system. Synapsis AI completes pre-screening on both unstructured and structured patient data, deploying medical logic with temporal sensitivity to dynamically match changing patient characteristics to complex clinical trial criteria within the exact window when a given EMR may qualify for study protocol criteria. If you like to innovate at the forefront of technology and build software that solves important real-world problems, we'd love to hear from you! At Dyania Health we are transforming the way in which machines understand and process medical information. We are seeking Senior Machine Learning / Software engineers who are passionate about their craft to help us in that mission. As a senior ML engineer at Dyania, you'll design, build, and deploy scalable ML-driven systems that power biomedical information processing. In this role, you will operate at the intersection of machine learning research and production-grade software engineering, owning the full lifecycle of ML-powered microservices — from model development and evaluation to deployment, monitoring, and continuous improvement. You will play a key technical leadership role, mentor junior engineers, and collaborate closely with product, UX, and clinical teams to translate real-world healthcare challenges into robust, scalable solutions.

Requirements

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.

Nice To Haves

Experience working with AWS or similar cloud platforms.
Experience working in agile development environments and familiarity with Jira.
Experience building ML systems in healthcare or other regulated environments.
Experience with monitoring, observability, and performance optimization in production ML systems.

Responsibilities

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.