Staff Software Engineer

Auxia; Bengaluru; Sur place; INR 3 000 000 - 5 500 000; Plein temps

Résumé du poste

Auxia is hiring a Software Engineer for Bengaluru, India to join our AI-driven platform. You will work on platform infrastructure, data systems, and backend services that power real-time decisioning for enterprise customers.

The role emphasizes scale, reliability, and fast iteration in a startup environment. We value strong fundamentals in distributed systems, data processing, and building production-grade systems.

Qualifications

• 9–12 years of experience in software engineering.
• Experience building and operating production backend systems at scale.
• Comfort with ambiguity in a startup environment.
• Strong product instinct and customer focus.

Responsabilités

• Platform Infrastructure — Kubernetes orchestration on GCP, service mesh, deployment automation, observability, cost optimization.
• Data Systems — High-throughput data pipelines and storage at scale.
• Backend Services — Kotlin/gRPC microservices, scoring engines, multi-tenant auth.
• ML Infrastructure — Model training pipelines and evaluation in production.
• Frontend & Developer Experience — Next.js admin console and CI/CD improvements.
• Customer Integration — Onboarding enterprise customers with SDKs and tooling.

Connaissances

Distributed systems

Data structures

System design

Production backend

Ambiguity tolerance

Product instinct

Outils

Kubernetes

gRPC

Kafka

Pub/Sub

Flink

Description du poste

Software Engineer — Bengaluru, India

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.

The Hard Problems

• 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?

• 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.

What You Bring?

• 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.