Software Engineer, Backend
Build and operate dependable agentic AI systems that analyze large-scale hardware telemetry for aerospace and defense teams. Design tools, execution environments, distributed job systems on Kubernetes, and evaluation frameworks while owning the product end-to-end and speaking directly with customers.
About the job
Responsibilities
- Talk directly to customers and partner with product to turn real review workflows into agent capabilities: generating dashboards, writing analysis scripts, and surfacing insights buried in their telemetry.
- Design, ship, and operate agentic systems that reason over large-scale time-series data and hardware domain context.
- Build everything around the model that makes agents dependable: tool interfaces, sandboxed execution for agent-generated code, context and memory management, custom compaction algorithms, opinionated skills, and guardrails.
- Build the distributed systems that let long-running agent work stream in real time, survive disconnects, and resume across restarts.
- Design job-based execution systems that schedule, scale, and tear down agent workloads on Kubernetes, both in the cloud and on-prem.
- Develop and maintain Sift’s MCP server, the tool surface that lets both our agents and our customers’ AI tools query telemetry directly.
- Design and implement the APIs that power our agentic capabilities.
- Build evaluation suites that measure whether agents actually help engineers, and instrument quality, latency, cost, and failure modes in production.
- Integrate and assess frontier models across providers.
Requirements
- 3+ years of professional software engineering experience.
- Excitement about owning a product area: talking to customers, deciding what to build, and shipping it.
- Experience building APIs (REST, gRPC, etc.) and complex backend services with technologies like Go, Python, Rust, or similar.
- Working knowledge of distributed systems fundamentals.
- Curiosity about new AI products: you try new agents, models, and features as they ship, and have opinions about what makes them good.
Nice-to-Haves
- Shipped products to users at scale: large data volumes, significant active user counts, or deep technical complexity.
- Shipped LLM-powered features.
- Built agentic systems: multi-step tool use, planning loops, context management, and evals.
- Designed tool ecosystems for agents, including MCP.
- Worked with sandboxed or isolated execution of generated code.
- Operated services in production (Kubernetes, observability, incident response).
- Worked with streaming or time-series data systems (Kafka, Flink, TimescaleDB).
- A personal ecosystem of AI dev tooling: custom agents, skills, scripts, or workflows built to ship faster.
- A background in time-series data, scientific computing, or hardware test and telemetry.
- Built internal agentic tooling that accelerates an engineering org.
Technologies
- Web frontend & backend: ECharts, Go, gRPC, PostgreSQL, Protobuf, Radix, React, Redux, and TypeScript.
- Data: Arrow, DataFusion, Flink, Parquet, and Rust.
- Infrastructure: Argo CD, AWS, Docker, GitHub Actions, Grafana, Kubernetes, Kustomize, Linux, Prometheus, and Terragrunt.
Compensation
Salary range: $150,000 - $200,000 per year. Plus equity and benefits.
Skills
Go, Python, Rust, Kubernetes, gRPC, Postgres, AWS, Kafka, Flink, Timescaledb, LLMs, Distributed Systems, APIs, Time-Series Data
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