Senior Full-Stack Software Engineer

Role Summary

What We Do

We build AI models to enable smaller, faster, and more successful clinical trials.

About Altis Labs

Altis Labs is a computational imaging company focused on improving how oncology trials measure treatment benefit. Our core technology is IPRO, an AI model that generates patient-level outcome predictions directly from routine medical imaging data. Our global biopharma customers use IPRO to predict efficacy, navigate billion-dollar development decisions with confidence, and move their most promising therapies through Phase I–III trials faster. IPRO is trained on the industry’s largest real-world imaging, clinical, and outcomes database, containing over 210 million longitudinal images and more than one million patient-years of linked outcomes.

Our multidisciplinary team of AI scientists, clinicians, and business operators is on a mission to get the most effective treatments to patients sooner. We collaborate closely with academic medical centers and co-publish our results at top-tier medical conferences.

Altis is headquartered in Toronto, serves 6 of the top 20 global biopharmaceutical companies, and is backed by leading life sciences and technology investors.

Altis Labs is recruiting a Senior Full-Stack Software Engineer with an entrepreneurial, product-focused mindset. You'll design and implement testable, scalable code as we expand our internal tooling and web-based software product — building critical features from the ground up and delivering efficiently without bureaucracy. We're looking for someone who can wear many hats. Concretely, you'll own large parts of two areas: Nota, our clinical annotation and data-curation platform, and the ML inference infrastructure that serves our imaging models in production. Responsibilities are dynamic and will expand as we grow to meet our team's and clients' needs.

• Our Stack
• Area Technologies: Nota React · Express.js · FastAPI · Hasura GraphQL · PostgreSQL · AWS ECS (Fargate) · RDS · Terragrunt
• Clinical DB: DuckDB · AWS S3 · Terraform
• Inference pipeline: Ray Serve on Kubernetes · PostgreSQL · AWS EKS · RDS · S3 · Terraform · ArgoCD
• Cross-cutting: GitHub Actions · Slurm · GCP · Azure · Docker

Responsibilities

• Design and build scalable applications for data acquisition, management, and visualization using best practices
• Build and maintain features across the full stack of Nota — from React components through GraphQL schemas to Postgres query performance
• Design, deploy, and scale ML inference services on Ray Serve and Kubernetes, including model versioning, autoscaling, and observability
• Architect and manage the cloud environment supporting data ingestion, querying, and computer vision / ML pipeline development and deployment
• Own infrastructure as code and GitOps across multiple accounts and environments (Terraform, Terragrunt, ArgoCD)
• Build CI/CD pipelines that let researchers ship models without filing tickets
• Handle medical imaging data at scale: DICOM ingestion, de-identification, format conversion, and reliable movement of terabytes
• Work closely with the product team and external stakeholders to shape engineering goals and requirements

Requirements

• 5+ years of software engineering experience with a track record of building and operating applications in production
• Full-stack depth across front-end, back-end, database design, and software/network security — equally comfortable debugging a React render loop and a Postgres query plan
• Production Kubernetes experience: designing what runs on it and why, not just kubectl apply
• Deep infrastructure-as-code practice with Terraform (Terragrunt a plus) and GitOps deployment patterns
• Comfort operating in mixed environments: multi-cloud (AWS primary, GCP/Azure secondary) and HPC schedulers like Slurm
• Fluency in Python and TypeScript/JavaScript; experience with React, GraphQL, and Docker
• Mature CI/CD instincts: GitHub Actions, reproducible builds, environment parity
• Excellent written and verbal communication skills in English

Nice to Have

• Experience in the medical technology domain (DICOM, PACS, de-identification, or clinical trial imaging)
• Familiarity with computer vision and machine learning applications/pipelines
• Experience with Ray (Ray Serve, Ray Data, or Ray Train) in production
• Experience serving deep learning models with real latency and throughput requirements
• Familiarity with regulated environments (HIPAA, GDPR, SOC 2, or FDA-regulated software)
• Bachelor's degree in engineering, computer science, or a related field

Agentic Engineering

We're an AI-native engineering team, and we treat this as a real skill rather than a résumé line. You should have opinions about:

• Working effectively with Claude Code (or comparable agentic tooling) on large, existing codebases
• Structuring repositories, context, and documentation so agents produce production-quality output
• Building custom tooling — MCP servers, skills, subagents, hooks — where it earns its keep
• Where agents should be trusted, where they should be reviewed, and how to tell the difference
• If you've been quietly reshaping how your team works because of these tools, we want to talk to you

Benefits

• Competitive pay and equity compensation
• Competitive medical, vision, and dental insurance coverage
• 4 weeks of vacation per year