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Founding Engineer, Agent and Data Systems

TenorSoftware
Pay
$150K–$200K
per year
Work mode
On-site
Full Time
Level
Senior

San Francisco, CA at a glance

Rent
#2 of 51
$2,680/mo+46% vs US avg
Weather
#17 of 51
295 mild days0 hot · 0 cold
Income tax
#1 of 51
13.3% top rateCalifornia

What you need

  • Building and operating LLM-driven agent systems
  • Hands-on harness engineering: tool execution, evals, replay, simulation
  • Strong backend and systems engineering fundamentals
  • Full-stack product instincts
  • Data engineering experience (pipelines, ETL, modeling, warehousing)

What you'll do

  • Design and operate agent harnesses, execution, replay, simulation, evaluation
  • Build backend systems for orchestration, state, versioning, observability
  • Build data systems: pipelines, ETL, modeling, warehousing
  • Ship full-stack product features across APIs, services, workflows
  • Collaborate with customers and founders to identify problems, ship quickly

About Tenor

Tenor (YC S26) is building the full-stack infrastructure for AI-native businesses, redefining how companies are built, launched, and scaled in the 21st century. We're a four-person, seed-stage startup hiring our fifth teammate. Our team brings together engineering, ML, and product experience from Tesla, Waymo, Amazon, UC Berkeley, and Stanford, including research published at ICML.

The role

We are hiring a founding engineer with end-to-end ownership across agent, data, backend, and product systems. You will engineer harnesses across our agent systems, including tool-use loops, evidence-driven learning systems, replay and simulation infrastructure, evaluation and promotion gates, and the backend and full-stack product systems that hold everything together.

You will work directly with the founders to turn prototypes into reliable product infrastructure and shape both the technical architecture and the product.

What you will have ownership in

• Design and operate reliable agent harnesses, execution systems, replay and simulation infrastructure, and evaluation and promotion gates.

• Build backend systems for orchestration, state, versioning, observability, and reliability.

• Build data systems, including pipelines, ETL, data modeling, and warehousing.

• Ship full-stack product features across APIs, services, and user-facing workflows.

• Work closely with customers and the founding team to identify problems, test ideas, and ship quickly.

What we are looking for

• Concrete evidence of building and operating LLM-driven agent systems, beyond calling a chat API.

• Hands-on harness engineering experience, including tool execution, evals, replay, simulation, state, and observability.

• Strong backend and systems engineering fundamentals.

• Full-stack product instincts and the ability to work wherever the product needs you.

• Data engineering experience with pipelines, ETL, modeling, or warehousing is a meaningful advantage.

• High agency and ownership. You start, ship, and improve things without waiting for detailed direction.

• Comfort with the pace and ambiguity of a very early-stage startup.

We care more about demonstrated ability than years of experience. Strong side projects, open-source work, publications, internships, or startup experience can all be compelling when they show relevant outcomes.

Location and compensation

This is a full-time, in-person role in San Francisco. We support relocation and US visa sponsorship. The base salary is $150,000 to $200,000, plus 0.5% to 1.5% equity.

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About Tenor

Your AI spend becomes accountable labor: every AI worker gets a job, persistent context, a budget, guardrails, and outcomes you can measure. Copilots made individuals faster. Agent builders made automations easier to create. Both leave the organization unchanged: same roles, same capacity, same org chart, and leadership still sees AI as a software expense, not labor. Tenor makes AI enter the company as labor instead: every employee manages a team of AI workers built around the recurring work of their role, each with a defined job, boundaries, a budget, and a record of what it delivered. Employees provide the judgment. AI workers provide the execution. Leadership finally sees what AI spend returns.

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