About Auxia
Auxia is an agentic AI platform that helps enterprises deliver personalized 1:1 customer journeys. Our Decision Agent determines the optimal message, timing, channel, and incentive for each individual user — processing 3B+ events/day, 25K+ queries/second, and 1B+ decisions/day at sub-100ms latency.
We serve global enterprises including Atlassian, The Guardian, Docomo, Comcast, Mercari, and MUFG Bank and many more across email, push, in-app, and messaging channels. Backed by $23.5M from VMG Technology Partners, Stage 2 Capital, and MUFG Innovation Partners.
Auxia was founded by a group of senior leaders in marketing and engineering at Google. Since then, we have brought on board skilled engineers, data scientists and more, from companies such as Meta, Amazon, Microsoft, Lyft and DoorDash.
We're scaling our organisation to match our domestic and global needs, and we need exceptional people to help us get there. That's where you come in.
The Hard Problems
Auxia isn't a typical SaaS platform. Here's what makes the engineering genuinely interesting:
Multi-tenant ML serving at scale. A single shared platform serves 20+ enterprise customers, each with millions of users, hundreds of treatments, and unique business constraints — all at sub-100ms p99. Every architectural decision has to balance isolation, performance, and cost across customers.
Real-time decisioning under uncertainty. Our Decision Agent picks the best action for each user from hundreds of options using bandits and other ML models, learning continuously from live interactions. Cold-start, exploration-exploitation tradeoffs, and feedback loops are daily problems.
Enterprise data integration. Every customer brings different data formats, volumes, schemas, and infrastructure (Snowflake, BigQuery, S3, CDPs). Onboarding a new customer's data pipeline needs to be fast and reliable — we're building toward fully automated ingestion.
Agentic AI systems. Our Analyst Agent autonomously builds data semantic layers, constructs and executes efficient queries, and runs complex analyses and generates insights for enterprise marketers., and generates insights for enterprise marketers. Building reliable, observable AI agents that interact with real production data is a frontier problem.
What You'll Work On
Depending on your interests and strengths, you'll work across some combination of:
Platform Infrastructure — Kubernetes orchestration on GCP, service mesh, deployment automation, observability (metrics, tracing, alerting), cost optimization across a multi-region platform.
Data Systems — High-throughput ingestion pipelines (Apache Beam/Dataflow, Pub/Sub, Airflow), BigTable and BigQuery at terabyte scale, real-time feature stores, data warehouse and reporting infrastructure.
Backend Services — Kotlin/gRPC microservices, treatment recommendation and scoring engines, experiment framework, configuration management, multi-tenant authorization.
ML Infrastructure — Model training pipelines (Metaflow), model serving, feature engineering automation, A/B test evaluation, diverse ML algorithms (including multi-armed bandits) in production.
Frontend & Developer Experience — Next.js admin console, internal tooling, developer productivity, CI/CD pipeline optimization.
Customer Integration — Forward-deployed engineering to onboard enterprise customers, building SDKs and integration tooling.
Our Tech Stack
Languages: Kotlin (primary backend), TypeScript/Next.js (frontend), Python (ML pipelines)
Infrastructure: GCP, Kubernetes, Docker, Terraform
Data: BigTable, BigQuery, Apache Beam/Dataflow, Pub/Sub, PostgreSQL
Services: gRPC/Protobuf, Spring Boot
ML: Metaflow, custom bandit/scoring frameworks
Tooling: Gradle, GitHub Actions, Linear, Figma
How We Work
AI-native development. We use Claude Code extensively — for code generation, architecture exploration, code review, debugging, and documentation. Engineers here ship faster because they're fluent with AI-assisted development. We're building…