HarperSan Francisco, CAAs a Staff Engineer, Harness Engineering, you will own the meta-harness that powers all Harper AI agents, both product and coding. This involves designing and building the agent loop, execution environment, tool layer, and model-provider abstraction to ensure efficient and reliable agent operation.
Harper operates like a factory with a series of modules spanning the full lifecycle from intake through renewals. Across them we run a stack of internal AI systems covering operator guidance, the operational backbone that matches risks to underwriters, autonomous communications, and voice AI for customer interactions.
Underneath all of that lives the harness substrate you'll own - the meta-harness wrapping our frontier coding agents, OpenClaw, Hermes, and the model-routing layer on top of our foundation-model providers. You define the contracts every Harper agent integrates against.
OTE: $253,000–$308,000 cash compensation (base salary + target performance bonus) Equity: competitive equity, so you share in the company you are helping build Location: San Francisco, in-office
Staff ML Systems Engineer leading large-scale embedding and recommendation model architecture, distributed training, and real-time serving. Owns ML strategy and mentors engineers on personalization systems.
Senior Staff Software Engineer building an AI agent platform and automated workflows to transform Coinbase's Legal organization. Architect production-grade LLM and multi-agent systems that replace manual legal processes such as agreement redlining and governance.
Senior individual contributor architecting and scaling agentic LLM systems that turn messy manufacturing data into reliable root-cause insights. Owns orchestration, retrieval, evaluation, and guardrails for non-deterministic production systems.
Conduct optimization research and implement large-scale constrained optimization models that drive real-time trading decisions, working across the full research lifecycle from theory to production. Requires PhD-level coursework and strong applied research background in optimization.
New/recent PhD to own RL and post-training for large-scale omni models. Build and scale the full RL/post-training stack including rollout, optimization, reward modeling, and evaluation for real-time audiovisual AI.