Applied AI Engineer

Build the context + orchestration layer for AI‑first revenue software

Why Revic AI

Revic is solving the biggest challenge companies face, growing revenue. Revenue teams squander great engineering because they prioritize or execute poorly. We’re tackling this by using AI agents that are built on top of our collective revenue memory.

We’re connecting messy, real‑world data into context that AI agents can learn from and act on autonomously. The goal: replace meta‑work with action that compounds revenue per rep—less digging through data and 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—you’ll like building here. We ship in tight loops, learn in public, and measure by customer impact. Backed by SYN Ventures, we're working with customers like BigID and Modern Health.

If this work gets you excited—and you want to leave a mark—let’s talk.

What you’ll do

• Create the collective memory: Ingest and unify data from many sources (CRM and far 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 then into actions. Define simple eval harnesses to measure quality.

• Deliver where users work: Expose capabilities through native surfaces (apps, chat, and integrations) in tight loops with product and GTM—reducing context switches and meta‑work.

• Prove outcomes: With product and customers, define success metrics (e.g. tasks auto‑completed, adoption/retention, pipeline lift; keep latency in check) and wire observability so we can ship → learn → iterate quickly.

• Balance cost & reliability: Tune accuracy, latency, and cost for agent runs and retrieval; design fallbacks and safeguards that keep the system dependable under real‑world load.

What you’ll bring

• Owner/builder mindset with product taste — you frame problems, choose the simplest path, and own outcomes.

• 4+ years building & owning backend/platform systems end-to-end, with 0→1 wins and measurable business impact.

• Curious by default; comfortable taking smart risks and turning fuzzy problems into shipped outcomes.

• You talk in terms of impact and trade-offs; decide with ~70% info; turn ambiguity into simple, testable systems.

• Experience stitching messy, multi‑source data into something a product can reason over; strong instincts for reliability, privacy, and multi‑tenant boundaries.

• Able to hit the ground running with Python and standing up cloud infrastructure.

• Nice to have: exposure to agent orchestration/planning, retrieval/graph‑shaped context, eval frameworks, and distributed systems at scale.

How we work

• Outcome‑first: We anchor on the seller’s job; stay close to customers; success = adoption, pipeline quality, time‑to‑value.

• Ship small, learn fast: Start simple; instrument; iterate with “sniff tests.”

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

Our stack

We build with: Python, FastAPI/GraphQL, PostgreSQL/DynamoDB, AWS, Kubernetes, Pulumi, Spark/Databricks, and event‑driven architectures — plus React for product surfaces. Familiarity helps, but isn’t required.

Logistics & offer

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

• Equity: meaningful ownership in a fast-growing company

• Benefits: health, dental, vision

• Location: Hybrid San Francisco, New York City, Vancouver — 3 days in‑office, 2 remote

• Team: small, senior; big surface area and ownership