Today, Gong’s Talent Acquisition team runs on manual processes and disconnected systems — candidate data in one tool, interview data in another, employee data in a third, and no way to see across any of it without someone stitching it together by hand This role exists to fix that permanently, with a system, not a person doing it manually You will not recruit. You won’t source candidates, screen resumes, or run interviews Your job is to build the engine underneath all of that: the automations, integrations, and AI-powered workflows that let recruiters and hiring managers actually see what’s happening and move faster, without you in the loop for every question This is a 0-to-1 build. There’s no existing talent engineering function here today — you’re standing it up wiring all of our disparate systems into a single governed layer that AI tools can actually reason over Success looks like shipped systems, not proposals: a dashboard that updates itself, a stuck-req alert that never needed a human to notice it, an agent that a recruiter or manager can interact with instead of having to manually find the solution You’ll be evaluated the same way an engineer is evaluated — by what you ship and the measurable difference it makes. Prior recruiting operations experience is useful context, not a requirement Unify our systems and make them useful. Connect and integrate Workday (HRIS), Greenhouse (ATS), Gem (sourcing), and BrightHire (interview intelligence) Build the talent intelligence repository. A governed data and context layer that lets AI tools answer questions using our real definitions and data, with proper permissions — not generic or hallucinated logic Build agentic solutions across the hiring lifecycle for things like:



Automated reporting on time in stage metrics Real-time visibility into bottlenecks in our workflow A conversational tool that lets talent leadership and managers self-serve pipeline status Surfacing previously-qualified candidates already in our ATS against new roles Interview quality and consistency signals from recorded interviews Predictive flags for at‑risk offers Recruiter workload and capacity modeling Real input on build‑vs‑buy evaluation for our talent tech stack. Work directly with your manager to assess where in-house tooling can replace or reduce reliance on outside vendors Own outbound sourcing and offer effectiveness. Apply structured experimentation to how we reach and hire candidates — testing messaging, timing, and sequencing to systematically improve response, interest and acceptance rates instead of guessing. Build agents that continuously monitor these conversion rates, flags when performance starts trending in the wrong direction before it becomes a bigger problem, and refines its own benchmarks and test design as more data comes in Partner, not duplicate. Work alongside our central People Analytics, HRIS, and IT teams — this role owns the talent-specific slice of data, automation, and tooling, operating inside the same governance model IT is building for technical/builder tool access company-wide You’ll report directly to the Senior Director, Talent Operations, with technical mentorship and architectural review provided through our central AI development team





Benefits

Mental health support resources Work from home stipend Generous vacation days Quarterly recharge company shutdowns Parental leave Employee equity Commuter & parking benefits Retirement savings Training & development programs Employee resource groups Mentorship opportunities Adaptable wellness fund

Experience designing and analyzing structured experiments — A/B or multivariate testing, in any domain — and the judgment to know when a result is real signal versus noise from too small a sampleComfort with ambiguity. This role is being defined as it’s built; you’ll help shape the roadmap, not just execute oneSound instincts around permissions and access-control design — you think about who should see what before you’re asked toReal experience building solutions that integrate with external systems — APIs, OAuth, webhooks, rate limits, error handling in production, not just in a tutorialSQL proficiency — you’re comfortable joining and querying data across systemsRecruiting or Talent Acquisition background is a plus, not a requirement. We’d rather hire a strong builder who can learn the domain than the reverseStrong scripting/backend skills (Python or TypeScript/Node)Hands‑on experience with LLM APIs and tool/function-calling patterns; direct experience with MCP (Model Context Protocol) is a strong plus, though not requiredExperience in an analytics engineering, automation engineering, or solutions engineering roleFamiliarity with recruiting or HR systems (Greenhouse, Workday, Gem, BrightHire, or similar)Interest or prior exposure to the emerging discipline of applying software engineering directly to talent acquisition and recruiting operations

#J-18808-Ljbffr

📌 Talent Engineer (Salt)
🏢 Gong
📍 Salt