Software Engineering Manager

VIDA Diagnostics•Edina, MN

About The Position

VIDA is the global leader in AI-powered Biomarke r Intelligence Sol utions. We use our secure, cloud-native medical imaging technology to create quantitative imaging biomarkers. Our engineering teams sit at the intersection of large -scale data processing, AI/ML, and distributed computer vision workloads. Role Overview As a Software Engineering Manager at VIDA , you will have a strong technical depth in cloud-native , distributed data processing systems – high-throughout , horizontally scalable architectures capable of processing millions to billions of events, managing fan-out/fan-in workflows, and orchestrating large-scale data movement across distributed com pute clusters . In this role , you will manage and g row a team of backend and platform engineers responsible for building VIDA’s distributed compute and data backbone: high-volume ingestion pipelines, scalable workflow engines, secure data storage layers, and AI/ML -supporting infrastructure for the VIDA Intelligence Platform and VIDA Biobank. You’ll partner closely with Product, Data Science, QA, Security, and O perations to deliver highly reliable , compliant, multi -tenant systems that support pharma trials , large-scale imaging research, and next-generation AI workloads .

Requirements

7+ years of experience building large-scale backend or distributed systems; 2+ years managing or leading technical teams.
Deep experience with cloud-native distributed architectures (AWS preferred).
Hands-on knowledge of: Event-driven systems (Kafka/Kinesis/ Pub Sub ) Micro services at scale (ECS /EKS/Lam bda) Distributed data storage (S3, RDS, DynamoDB, Delta Lake) Workflow orchestration frameworks
Strong system design and distributed systems fundamental s: consensus, partitioning , caching, backpressure, idem potency, retries, stream processing.
Proficient in modern programming languages (Python, Go, Java, or similar).
Experience building or managing multi-tenant SaaS sy s tems with strict SLAs.
Experience operating systems in compliance-sensitive environments (healthcare, finance, enterprise SaaS).

Nice To Haves

Experience with medical imaging, DICOM, or AI/ML data processing.
Familiarity with lakehouse architectures, Databricks, large-scale batch compute platforms.
All VIDA employees are expected to be flexible and have an entrepreneurial mindset.  Other duties may be assigned as needed.

Responsibilities

Team Leadership & Delivery Lead, mentor, and develop a team of software engineers focused on backend services, distributed systems, and cloud infrastructure.
Build and engineering culture grounded in reliability, observability, automation, and high accountability.
Drive execution of critical platform initiatives: scalable DICOM ingestion, distributed workflow orchestration, batch data processing pipelines, and date lake/ lakehouse systems .
Ensure Engineering best practices across code quality , testing, CI/CD, IaC , and operational readiness.
Technical Ownershi p & Arch itecture Provide strong hands-on technical guidance in system design, distributed systems patterns, and cloud-native architecture.
Own the reliability and performance of distributed components such as: Event-driven pipelines (Kinesis, Kafka, Pub/Sub equivalents ) Workflow orchestration (Step Functions, Temporal, Airflow) Scalable microservices for multi-tenant data access
Collaborate with senior architects and the VP of Engineering on platform roadmaps and architectural evolution.
Champ ion modern distributed systems practices including idempot ent operations, partitioning, c ach ing, backpressure management, and autoscaling.
Cross-Functional Collaboration Work with Product Management to break down features into clear technical plans and deliverable increments.
Partner with Data Science/ML teams to support model training, inference workflows, and large-scale compute jobs.
Coordinate with Security, Compliance, and DevOps to ensure systems meet HIPAA, SOC2, GDPR, and GxP standards.
Collaborate with Customer Success and Support teams to diagnose issues and improve platform-level SLAs.