Senior Full-Stack Software Engineer

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. The Role 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 Qualifications 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