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.
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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.
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 data processing 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 internal AI agents to automate parts of the DS and engineering workflow. If you're excited about working at the intersection of building AI products and using AI to build, this is the place.
Small team, high ownership. ~30 engineers across US, India, and Japan. No layers between you and production. You'll own systems end-to-end — design, build, deploy, monitor.
Onsite. We believe the best engineering happens in-person, especially at our stage. Fast iteration, whiteboard sessions, and same-room debugging.
Ship weekly, not quarterly. Ideas go to production in days, not months. We make smart tradeoffs between speed and quality — and we trust engineers to make those calls.
Strong fundamentals in distributed systems, data structures, and system design with 9-12 years of experience.
Experience building and operating production backend systems — you've dealt with the messy reality of scale, not just the theory.
Comfort with ambiguity. At a startup, you'll sometimes define the problem before solving it.
Product instinct. You think about why something matters to the customer, not just how to build it.
Bonus:
Experience with Kotlin, gRPC, or Kubernetes
Experience with real-time streaming (Kafka, Pub/Sub, Flink)
Experience with ML infrastructure or recommendation systems
Prior startup experience
Auxia is committed to building a diverse and inclusive workplace. We welcome applicants from all backgrounds. Interested? Email [email protected].
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Job details are sourced from the employer's original posting.
Open job postingAbout the company
Auxia is building the Agentic Customer Journey Orchestration Platform, redefining how enterprises activate, engage, and retain their customers through intelligent, adaptive AI systems.