Cora AI — Founding Forward Deployed Engineer

Type: Full-time | Hybrid | SF Bay Area, CA (LA also considered) Compensation: $180K–$230K base + competitive equity Hiring count: 1 Visa sponsorship: No — not open to any visas (US citizens / Green Card holders only) Reports to: Jesal Gadhia, Co-founder & CTO

About Cora AI

Cora AI is rebuilding B2B customer operations from the ground up with AI agents, orchestrating a blend of digital and human touchpoints to deliver high-touch service at scale. Rather than augmenting humans with chatbots, Cora deploys long-running agents across the post-sale customer lifecycle. The founding team previously built generative AI and automation platforms at BetterUp, LinkedIn, and Thoughtful AI.

Founded: 2025 | Team size: 6 | Total funding: $4M Industry: AI, B2B, Enterprise Website: www.cora.ai Office: San Francisco, CA Investors: Acrew Capital, Emerson Collective, Recall Capital, plus angels/execs from LinkedIn, BetterUp, Glean, Scale AI, and Gusto Customers: BetterUp, Fountain, Dialpad, Coursera, Flock Safety

Why Candidates Should Join

• 3rd-time founder at the helm: CTO has scaled engineering orgs to 100+ engineers; company backed by Acrew Capital and Emerson Collective with angels from LinkedIn, BetterUp, Glean, Scale AI, and Gusto.
• Real traction: Enterprise customers already include BetterUp, Fountain, Dialpad, Coursera, and Flock Safety.
• Right problem, right moment: Most AI investment has chased support chatbots and pre-sales prospecting. Cora is tackling the messy middle — long-running agents for post-sale lifecycle management (onboarding, QBRs, renewals, churn).
• High ownership + career slope: Own large chunks of product and infra, make real architectural calls, and shape engineering culture, with weekly feedback loops, fast promotions, and deep exposure to applied AI (voice agents, memory systems, knowledge graphs, agentic workflows).

Intake Call Summary

• Company is ~1–1.5 years old, positioning itself as a customer command center; using AI agents to rebuild CX functions rather than following traditional models.
• Founding full-time hire to relieve deployment bottlenecks and drive customer enablement — expectation is setting up platforms, writing prompts, and accelerating deployments.
• Wants technical skill combined with strong customer-facing ability; hands-on experience with AI, agent building, and prompt writing is essential.
• Culture values fast shipping, simplicity, and strong communication with high EQ.
• Salary $180K–$230K, flexible for exceptional candidates; hybrid with a preference for California-based candidates; no visa sponsorship.
• Urgent need — wants to fill ASAP. Process: manager interview technical case study CEO interview.
• Ideal profile blends big-tech experience with startup roles (preferably AI startups); high agency, comfort with ambiguity, and leadership growth potential.
• Success = timely, successful customer deployments with no major account issues and clear customer communication.
• Red flags: frequent job changes with no promotion history; candidates from traditional, slower-moving tech or consulting firms.

(Note: the auto-transcript rendered the company name as "Quora" — corrected to Cora throughout.)

The Role

A Founding Forward Deployed Engineer who embeds directly with enterprise customers to bring Cora's AI agents to life in production, owning accounts end-to-end and feeding everything learned in the field back into the core product.

What You'll Be Doing

• Embed with customers to understand how they run onboarding, adoption, and renewals, then map where Cora's agents drive the most value
• Deploy and configure customer agents against real customer systems: connecting Account Brain to data sources (BigQuery, Salesforce, warehouses), wiring up SSO and integrations, and getting agents into production
• Build evals to prove agent quality before shipping, using real customer data, and hill-climb until agents clear the bar
• Partner with GTM to close, activate, and expand accounts — you're the technical credibility in the room
• Feed field learnings back into the core product; when the platform gets in the way, fix the platform
• Debug live deployments and run root-cause analysis when an agent misbehaves in production
• Own the smallest end-to-end slice that proves value, then expand from there

Tech stack: Python, FastAPI, React, PostgreSQL, LLMs, RAG, BigQuery, CI/CD, Agent frameworks

Qualifications

Seniority

• 2–7 years of experience in full-stack software engineering and/or a forward-deployed engineering role [Required]

Work Experience

• Senior archetype (5–7 yrs): 2+ YOE software engineering and 2+ YOE forward-deployed engineer at a top company/startup [Must have]
• Junior archetype (2–3 yrs): strong internships, early promo trajectory at a top company, or founding engineer at a YC-backed startup [Must have]
• Shipped end-to-end AI/LLM features in production at a startup (not just prototypes) [Must have]
• Worked directly in customer environments (forward deployed, solutions engineering, or similar) [Required]
• Strong Python with FastAPI (backend) and React (frontend) skills [Required]

Education

• BS in Computer Science, Engineering, or equivalent demonstrated by shipping real production systems [Required]

Miscellaneous

• Based in or willing to relocate to the SF Bay Area [Must have]

Traits to Avoid

• Pure Big Tech / FAANG engineers with no startup or customer-facing deployment experience
• Slow career slope — no promotion in the past 2–3 years
• Job hopping

Role Details

• Salary | $180K–$230K
• Equity | Competitive
• On-site policy | Hybrid, SF Bay Area (~once a month in person; CEO is LA-based, so LA candidates also considered)
• Visa sponsorship | No — US citizens / Green Card holders only
• Employment type | Full-time
• Location | SF Bay Area, CA (also San Francisco, CA and Los Angeles, CA)

Screening Questions

1. Describe a time you deployed an AI or LLM-based system into a customer's production environment. What did you build, what constraints did you run into, and how did you ship it?
2. Can the candidate be on-site? If not, is the candidate willing to relocate?
3. What is their salary expectation?
4. How actively is this candidate exploring new opportunities?

Interview Process

Stage 1 — Submit candidate After submitting, you'll be notified if the hiring manager wants to proceed.

Stage 2 — Behavioral Interview w/ CTO (Jesal Gadhia) (30 min) Conversation with the CTO to discuss the candidate's experience and culture fit.

Stage 3 — Case Study w/ CTO (Jesal Gadhia) (60 min) Candidate receives a take-home technical case study prior to the interview. During the interview, they discuss their approach; the CTO probes problem-solving skills.

Stage 4 — Final Round w/ CEO (Amir Ghowsi) (30 min) Final conversation with the CEO. Covers leadership alignment, ownership mindset, approach to ambiguity, and long-term vision for the deployment function, including how the candidate would shape the deployment practice over time.

Stage 5 — Reference Checks

Stage 6 — Offer Extended

Stage 7 — Candidate Hired

Ideal Companies & Backgrounds

Updated Jul 22, 2026

Forward Deployed Engineering / Field Engineering companies (Palantir-style deployment models) Databricks, Anduril Industries, Scale AI, Weights & Biases, Moveworks, Glean, Cohere, Snowflake, Notion, Distyl, Ramp

Non-ideal companies — traditional slow-moving large tech (pure Big Tech, no startup/customer-facing experience) Oracle, IBM, Cisco, SAP, HP, Dell Technologies, VMware

Ideal Candidate Profiles

For reference only — do not source these specific profiles.

Dustin Zhu — LinkedIn Deployment Engineer | Palo Alto, California

• Duke (strong school) with strong experience in AI and as an FDE
• FDE at Genspark (Jan 2025–present) — AI agents for knowledge workers; company went $0–250M ARR in 12 months
• Deployments & Ops at Probook (employee #3, Sequoia + a16z backed) and Dev Shop Founder at Zephron (workflow automation)
• Applied AI at Accenture earlier in career; BS Computer Science & Economics, Duke University

Rejected Candidate Feedback

• Focus on production-grade AI/LLM deployments — ensure candidates have shipped end-to-end, customer-facing AI solutions, not just prototypes.
• Target top-tier startup FDEs — prioritize profiles with 0–1 startup experience over pure Big Tech or non-customer technical roles.
• Eliminate weak customer engagement and job hopping — screen out candidates lacking sustained, hands-on customer deployment and technical ownership.
• HM update (Aug 4, 2026): Would like to see more FDE profiles from top-tier AI startups.