below marketmost similar roles pay hereabove market
This role pays less than
55%
of similar roles. Most pay
$166,075–$239,800
— the blue band above.
At the midpoint, this role pays about
$196k
versus about
$203k
for comparable roles.
Based on 240 similar postings.
Employer
About Abbott
Abbott Laboratories is a global healthcare company that manufactures and markets a broad and diversified line of health care products including diagnostics, medical devices, nutritionals, and branded generic pharmaceuticals. Industry: Healthcare & Medical Devices
Abbott currently has
193 open roles
on FindRole.
Listed pay typically runs
$99,300–$198,700
across 123 roles with salary data.
The Senior Staff ML Ops Engineer provides technical leadership for the Medical Devices Digital AI initiatives, driving the architecture, scalability, and operational excellence of enterprise AI platforms. This role bridges advanced AI innovation with production-grade engineering by defining technical strategies, establishing MLOps best practices, and leading the design of robust, scalable AI infrastructure. You will architect highly available cloud and on-prem hybrid ML infrastructure for data ingestion, feature engineering, training, and monitoring. Key responsibilities include implementing CI/CD workflows, ensuring compliance with HIPAA, GDPR, and SaMD guidelines, and mentoring engineers. Required skills include experience with AWS, Azure, Google Cloud, Docker, Kubernetes, Airflow, Terraform, Databricks, Spark, and SQL. You will solve complex problems regarding the deployment of reliable, secure, and compliant AI solutions within a regulated medical device environment.
Architect a highly available, secure, and scalable cloud/on-prem hybrid ML infrastructure.
Act as the primary technical bridge between ML science teams and engineering teams.
Implement robust CI/CD workflows for ML models, including testing, rollout, and rollback strategies.
Ensure strict compliance with regulatory standards such as HIPAA, GDPR, and SaMD guidelines.
Mentor ML and software engineers while establishing coding standards and conducting architectural reviews.
Drive the adoption of architecture strategy and AI governance with business leaders.
Develop infrastructure for experiment tracking, model training, data versioning, and model serving.
What we're looking for
Bachelor's Degree in Computer Science, Engineering Mathematics, or a related field.
Experience, or a Master's Degree with experience, or a Ph.D. with experience.
Deep experience building ML infrastructure for experiment tracking, model training, data versioning, annotation tools, model serving, monitoring, and observability.
Experience with GenAI and Agentic AI development infrastructure including grounding, RAG, MCP, and prompt engineering.
Experience developing AI products on cloud platforms, containerization technologies (Docker, Kubernetes), pipeline orchestration (Airflow), and IaC (Terraform).
Proficiency in tools like Azure ML SDK, Azure Data Factory, Databricks, Spark, and database technologies (SQL, NoSQL).
Significant experience with agile software development methods, CI/CD pipelines, and microservices architecture.
Experience working in an FDA-regulated business, including 510(k) submissions and clinical-grade ML product development (preferred).
Java
Python
Go
JavaScript
TypeScript
Swift
GenAI
Microservices
SRE
Distributed Systems
API Design
Full-Stack
SPA Frameworks
Data Processing
SLO Management
Solution Architecture
Golang
Kubernetes
Microservices
Distributed Systems
Workflow Orchestration
Durable Execution
Platform as a Service
Observability
Developer Tooling
Fault Tolerance