Founding Backend Engineer (Sales tech)

Hiring Hub; San Francisco (CA); Hybrid; USD 100,000 - 200,000; Full time

Benefits offered by this job

Meaningful equity

Small, senior team environment

Job summary

A leading technology recruitment firm is seeking a Founding Backend Engineer to create AI-driven revenue software. This hybrid role involves building a collective memory from data sources, designing systems for user engagement, and proving outcomes through metrics. The ideal candidate has 4+ years in backend systems, strong product sense, and is skilled in Python. This position offers a base salary range of $100k to $200k and meaningful equity in the company.

Qualifications

• 4+ years of experience in backend/platform systems.
• Experience handling multi-source data.
• Ability to deliver products with measurable impact.

Responsibilities

• Ingest and unify data into a context graph.
• Design and implement agentic systems.
• Partner with product and customers to define success metrics.

Skills

Backend development

Python

Cloud infrastructure

Data orchestration

Data privacy

Job description

About the job Founding Backend Engineer (Sales tech)

Location: Hybrid in San Francisco, New York City or Vancouver

Work type: 3 days in office, 2 days remote

Our client's team is building an AI-first revenue platform a collective intelligence layer that connects messy, real-world data into context that AI agents can learn from and act on autonomously. The goal is simple: replace metawork with action that compounds revenue per rep less digging through dashboards, more time moving pipeline.

If you're entrepreneurial and want to help define what AI-native software looks like how data becomes context, how agents plan and act, and how chat turns into outcomes this is the place to build. The team ships in tight loops, learns in public, and measures success by customer impact.

The Role: Founding Backend Engineer

As a Founding Backend Engineer, you'll play a central role in building the context and orchestration layer for AI-driven revenue software. This is an opportunity to take ownership of critical systems and shape the foundation of a fast-growing product.

What You'll Do

Create the collective memory: Ingest and unify data from multiple sources (CRM and beyond) into a semi-structured context graph that captures what leads to winning deals, modeled at multiple levels with strong tenant isolation.

Orchestrate agentic systems: Design planner/executor patterns, tools, and policies (including MCP-style interfaces) that turn context into content, and content into actions. Define lightweight evaluation harnesses to measure quality.

Deliver where users work: Expose capabilities through native surfaces (apps, chat, integrations) in tight loops with product and GTM teams reducing context-switching and metawork.

Prove outcomes: Partner with product and customers to define success metrics (e.g. tasks auto-completed, adoption, retention, pipeline lift, latency) and wire observability so the team can ship, learn, and iterate quickly.

Balance cost & reliability: Optimize accuracy, latency, and cost for agent runs and retrieval. Design fallbacks and safeguards to ensure the system remains dependable under real-world load.

What You'll Bring

Owner/builder mindset with strong product sense you frame problems, choose simple paths, and own outcomes.

4+ years of experience building and owning backend/platform systems end-to-end, with measurable business impact.

Ability to turn fuzzy problems into shipped outcomes; comfortable making decisions with ~70% information.

Experience stitching together messy, multi-source data into something a product can reason over. Strong instincts for reliability, privacy, and multi-tenant architecture.

Hands-on with Python and deploying cloud infrastructure.

Nice to have: exposure to agent orchestration/planning, retrieval or graph-shaped context, evaluation frameworks, and distributed systems at scale.

How the Team Works

Outcome-first: Anchored on sellers jobs; close to customers; success = adoption, pipeline quality, and time-to-value.

Ship small, learn fast: Start simple, instrument early, iterate with quick tests.

High trust, high ownership: Own problems end-to-end and make product-level decisions with the team.

Data/ML: Spark, Databricks, event-driven architectures

Frontend: React
(Familiarity is helpful, but not required.)

Compensation & Logistics

Base salary: $100k $200k (based on experience)

Equity: meaningful ownership in a fast-growing company

Location: Hybrid San Francisco, New York City, Vancouver (3 days in office, 2 remote)

Team: Small, senior group with big ownership and high impact