About this role
Architecting and scaling machine learning infrastructure for enterprise AI systems takes up the majority of the daily routine. PathAI focuses on improving pathology methods through AI to accelerate drug development and diagnostics, and this team builds the foundational tooling that supports those goals. Candidates who thrive here enjoy tackling complex technical challenges around reliability, collaborating across disciplines, and taking ownership of developer platforms.
PathAI needs a seasoned engineer to bridge the gap between research and production by designing automation and infrastructure across AWS and on-premises environments. The person stepping into this seat will guide architectural discussions, evaluate new tooling, and mentor junior teammates while keeping performance and security top of mind. Anyone who combines deep backend expertise with a genuine interest in operationalizing machine learning models will find a natural home in this group.
What you will do
- Architect and build infrastructure and automation in AWS and on-premises to support machine learning application development and deployment.
- Drive system design and lead architectural discussions for the MLOps suite while ensuring performance, security, and compliance requirements are met.
- Research, evaluate, and implement new MLOps tools, frameworks, and best practices to lead technical initiatives.
- Collaborate with machine learning engineers, data scientists, product engineering, and infrastructure teams to bridge the gap between research and production.
- Optimize machine learning workflows to ensure models are efficiently and reproducibly deployed and monitored.
- Champion engineering excellence by enforcing high coding standards, conducting design reviews, and mentoring junior engineers.
- Automate machine learning operations including CI/CD for models, feature engineering pipelines, and deployment strategies using Kubernetes, Airflow, and other orchestration tools.
What you need
- Hold a Bachelor's or Master's degree in Computer Science, Computer Engineering, Software Engineering, or a closely related field.
- Bring five or more years of software engineering experience with a focus on building production-grade frameworks or applications.
- Demonstrate strong software engineering skills in complex, multi-language systems alongside experience with scalable backend architecture.
- Possess hands-on experience with Kubernetes and cloud computing platforms, with a preference for AWS.
- Show familiarity with observability and monitoring tools such as Prometheus, Grafana, or Datadog.
- Have a solid understanding of DevOps principles and infrastructure-as-code tools like Helm and Terraform.
- Display experience owning development platforms and serving internal customers.
- Maintain a solid level of proficiency in Python along with exposure to additional languages.
Nice to have
- Experience with ML frameworks like PyTorch or Scikit-learn
- Experience with data workflow orchestration frameworks such as Airflow or Kubeflow
- Expertise in MLOps principles including model lifecycle management, feature stores, model monitoring, and CI/CD for ML
- Experience with streaming data processing like Kafka, Flink, or Spark Streaming
- Familiarity with security and compliance best practices in machine learning systems
Skills and tech stack
Questions candidates ask
What is the location and remote work policy for this role?
This is a hybrid position based in Boston, MA. However, a remote option may be considered for an exceptional candidate.
What is the expected salary range for the position?
The expected salary range based on the primary location in Boston, MA is $127,500 to $195,500. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law.
Are relocation benefits available?
Relocation benefits are not available for this position.