{"id":202809,"url":"https://alion.io/job/auxia-staff-software-engineer","title":"Staff Software Engineer","company":{"id":95601,"name":"Auxia","domain":"auxia.io","url":"https://alion.io/company/auxia","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-26T05:57:25Z"}},"role":"Backend","role_family":"Backend","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":37000,"max_usd":91000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":26},"experience_years_min":9,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"AI Agents","optional":false},{"name":"Airflow","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Kafka","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Feature Store","optional":false},{"name":"Flink","optional":false},{"name":"GCP","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google Bigtable","optional":false},{"name":"gRPC","optional":false},{"name":"Kotlin","optional":false},{"name":"Kubernetes","optional":false},{"name":"Metaflow","optional":false},{"name":"Next.js","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Service Mesh","optional":false},{"name":"Snowflake","optional":false},{"name":"bandit","optional":true},{"name":"Docker","optional":true},{"name":"Figma","optional":true},{"name":"GitHub Actions","optional":true},{"name":"Gradle","optional":true},{"name":"Java","optional":true},{"name":"JavaScript","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Python","optional":true},{"name":"React.js","optional":true},{"name":"Spring Boot","optional":true},{"name":"Terraform","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-02-23T11:59:21Z","employer_posted_date":"2026-02-23","last_verified_at":"2026-10-26T22:53:29Z","board_verified":true,"closed_at":null,"days_open":245,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":244},"description":"Software Engineer - Bengaluru, India\nAbout Auxia\nAuxia 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.\nWe 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.\n----------------------------------------------\nThe Hard Problems\nAuxia isn't a typical SaaS platform. Here's what makes the engineering genuinely interesting:\nMulti-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.\n\nReal-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.\n\nEnterprise 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.\n\nAgentic 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.\n\nWhat You'll Work On?\nDepending on your interests and strengths, you'll work across some combination of:\nPlatform Infrastructure - Kubernetes orchestration on GCP, service mesh, deployment automation, observability (metrics, tracing, alerting), cost optimization across a multi-region platform.\n\nData 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.\n\nBackend Services - Kotlin/gRPC microservices, treatment recommendation and scoring engines, experiment framework, configuration management, multi-tenant authorization.\n\nML Infrastructure - Model training pipelines (Metaflow), model serving, feature engineering automation, A/B test evaluation, diverse ML algorithms (including multi-armed bandits) in production.\n\nFrontend & Developer Experience - Next.js admin console, internal tooling, developer productivity, CI/CD pipeline optimization.\n\nCustomer Integration - Forward-deployed engineering to onboard enterprise customers, building SDKs and integration tooling.\n\nOur Tech Stack\nLanguages: Kotlin (primary backend), TypeScript/Next.js (frontend), Python (ML pipelines)\n\nInfrastructure: GCP, Kubernetes, Docker, Terraform\n\nData: BigTable, BigQuery, Apache Beam/Dataflow, Pub/Sub, PostgreSQL\n\nServices: gRPC/Protobuf, Spring Boot\n\nML: Metaflow, custom bandit/scoring frameworks\n\nTooling: Gradle, GitHub Actions, Linear, Figma\n\nHow We Work\nAI-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.\n\nSmall 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.\n\nOnsite. We believe the best engineering happens in-person, especially at our stage. Fast iteration, whiteboard sessions, and same-room debugging.\n\nShip 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.\n\nWhat you bring?\nStrong fundamentals in distributed systems, data structures, and system design with 9-12 years of experience.\n\nExperience building and operating production backend systems - you've dealt with the messy reality of scale, not just the theory.\n\nComfort with ambiguity. At a startup, you'll sometimes define the problem before solving it.\n\nProduct instinct. You think about why something matters to the customer, not just how to build it.\n\nBonus:\nExperience with Kotlin, gRPC, or Kubernetes\n\nExperience with real-time streaming (Kafka, Pub/Sub, Flink)\n\nExperience with ML infrastructure or recommendation systems\n\nPrior startup experience\n\nAuxia is committed to building a diverse and inclusive workplace. We welcome applicants from all backgrounds. Interested? Email .\nWe 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. 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