About AnoSys
AnoSys is an AI Operational Intelligence Platform for teams running production AI systems. We connect observability signals, agent traces, evals, cost, user behavior, governance context, and business outcomes so customers can detect silent regressions, explain non-deterministic behavior, control spend, and route action to the right owner. Backed by leading investors and built by infrastructure and AI veterans, AnoSys is expanding AI observability into the operating layer for agentic software.
Who We're Looking For
We're a small, high-impact team, and every person here shapes the product, the culture, and the trajectory of the company. We look for intellectually curious individuals who combine critical thinking with meticulous attention to detail — people who can identify problems early, reason through ambiguity, and solve challenges independently.
If you thrive in fast-paced, high-ownership environments — where your work directly shapes a category-defining product — we'd love to hear from you.
About the Role
AnoSys processes billions of telemetry events every day — traces, logs, metrics, and model evaluations generated by AI agents and LLM-powered applications running in production. As a Backend Engineer, you will design, build, and operate the core infrastructure that makes this possible.
You will work across the full backend stack: from high-throughput ingestion services that handle millions of events per second, to real-time stream processing pipelines that power anomaly detection and trace correlation, to the query engines and APIs that customers depend on for interactive exploration and alerting. You will tackle problems at the intersection of distributed systems, data-intensive computing, and applied machine learning infrastructure.
This is a foundational engineering role. The systems you build will directly determine the reliability, performance, and scalability of the AnoSys platform. You will have significant ownership and autonomy — from system design through production deployment — and your decisions will shape the technical trajectory of the company.
What You'll Do
- Design and implement scalable, fault-tolerant APIs and microservices for telemetry ingestion, trace correlation, anomaly detection, and real-time alerting
- Build and optimize high-performance data pipelines capable of processing millions of structured and semi-structured events per second with low latency
- Architect distributed systems patterns including event streaming (Kafka, Pub/Sub), partitioned storage, write-ahead logs, and eventually consistent data stores
- Optimize query performance across time-series, columnar, and graph-like data models for both real-time dashboards and deep analytical workloads
- Contribute to platform reliability through infrastructure-as-code, CI/CD pipelines, observability instrumentation, capacity planning, and incident response
- Collaborate with research scientists and product engineers to integrate ML models — including anomaly detectors and LLM evaluators — into production-grade serving infrastructure
- Participate in system design reviews, code reviews, and technical planning to maintain a high bar for engineering quality across the team
What We're Looking For
- 3+ years of backend engineering experience building and operating production-grade distributed systems at scale
- Strong proficiency in Python, Go, or Rust — with a willingness to learn new languages and frameworks as the technical landscape evolves
- Hands-on experience with message queues and streaming systems (Apache Kafka, Google Pub/Sub, Amazon Kinesis), columnar or time-series stores (BigQuery, ClickHouse, Apache Druid), and cloud infrastructure (GCP, AWS, or Azure)
- Solid understanding of distributed systems fundamentals — consistency models, partitioning strategies, replication, consensus, and failure modes
- Experience with containerized deployments (Docker, Kubernetes) and modern DevOps practices including infrastructure-as-code (Terraform, Pulumi) and CI/CD automation
- Familiarity with our Platform and the AI Observability space
- A detail-oriented mindset with a passion for writing clean, well-tested, and performant code — and a strong sense of ownership over the systems you build
- Strong written and verbal communication skills, with the ability to articulate complex technical concepts to both engineering and non-engineering stakeholders
Nice to Have
- Experience building or operating observability, monitoring, or APM infrastructure (e.g., at companies like Datadog, Honeycomb, Grafana Labs, or Chronosphere)
- Familiarity with OpenTelemetry, distributed tracing specifications, or telemetry data modeling
- Experience with ML serving infrastructure, feature stores, or model evaluation pipelines
- Contributions to open-source infrastructure or data systems projects
Apply for This Role
Prefer to attach your resume? Email us at hiring@anosys.ai
