# Senior Software Engineer, Backend

**Company:** [Siftstack](https://hotfix.jobs/companies/siftstack)
**Location:** Marina Del Rey, CA, San Francisco, CA
**Role:** ML Engineering
**Salary:** $200k – $250k/yr
**Experience:** 8+ years
**Skills:** Go, Python, Rust, Kubernetes, gRPC, Postgres, AWS, Kafka, Flink, timescaledb, LLMs, Distributed Systems, APIs, time-series data
**Posted:** 2026-07-23

> Build and operate dependable agentic AI systems that analyze large-scale hardware telemetry for engineering teams. Own product areas end-to-end, from customer conversations to designing tools, APIs, distributed execution on Kubernetes, evaluation frameworks, and integrating frontier models. Requires 8+ years software engineering experience with backend services and distributed systems.

## Job Description

## 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
- 8+ years of professional software engineering experience.
- Get excited about owning a product area: talking to customers, deciding what to build, and shipping it.
- Have built APIs (REST, gRPC, etc.) and complex backend services with technologies like Go, Python, Rust, or similar.
- Have a working knowledge of distributed systems fundamentals.
- Are curious 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.

## Compensation
- Salary range: $170,000 - $220,000 per year. Plus equity and benefits.

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