NOTE: Only EXCEPTIONAL candidates will be considered for this role.

Giga — Staff Backend Engineer

Type: Full-time | On-site | San Francisco, CA Compensation: $290K–$350K + competitive equity Hiring count: 6–8 Visa sponsorship: Yes — sponsorship and transfers available Reports to: Sebastian Gil

About Giga

Giga is an AI company building real-time AI agents for enterprise customer experience — agents that understand emotion, resolve issues instantly, and operate across voice, chat, and email in high-stakes regulated environments. Its long-term mission is to become the go-to platform for all enterprise workflow automation. The product is in market with Fortune 500 customers (e.g. DoorDash) deployed and paying, processing 1M+ customer sessions monthly with millions in revenue.

Founded: 2023 | Team size: 75 | Total funding: $61M (Series A) Industry: AI, Enterprise, Software Development, B2B Website: gigaml.com Office: Dogpatch, San Francisco

Why Candidates Should Join

• Strong backing and traction: $61M Series A backed by YC President Gary Tan, Nexus Venture Partners, and other top-tier investors. Fortune 500 customers already deployed and paying — not pre-revenue.
• Proven in market: Processing 1M+ customer sessions monthly with millions in revenue; has never lost a head-to-head against Sierra or Decagon.
• Founding-caliber team: Engineering led by IIT grads who published YC research on fine-tuning open-source models to match Anthropic/Meta quality. VP of Engineering is employee #4 from Snap (Stanford undergrad + grad).
• Real ownership: Staff engineers own entire product areas end-to-end — from system architecture to shipping code — with no layers of management between them and impact.

Intake Call Summary

• Giga is an AI company focused on enterprise workflow automation — "automate the world's work." Series A raised with millions in revenue and strong product-market fit.
• Hiring across staff backend, senior backend, and frontend infrastructure engineers. This role is the Staff Backend track: 7+ years backend engineering with strong system architecture.
• Core responsibilities: designing large-scale distributed systems and leading complex, ambiguous projects.
• Ideal candidate has a blend of big tech and high-growth startup experience, clear career progression ("slope"), and a strong technical foundation.
• Team includes engineers from top companies (e.g. Snap) and Stanford alumni; collaborative culture blending technical and customer-facing work.
• Comp: staff backend up to ~$400K, senior from ~$285K. On-site SF 5 days/week.
• Interview: recruiter screen technical screen onsite (system design + agent-building exercise + cultural fit).
• Timeline is urgent and aggressive — looking to hire ~10 staff-level backend engineers by end of quarter, with fast interview-to-offer turnaround.
• Pain point: difficulty finding high-caliber engineers who can scale infrastructure (scaling, multi-tenancy) and work hands-on with AI models and distributed systems.

Client signals from hiring manager announcements:

• Jun 15, 2026: Avoid healthcare tech at large — pharma, biosciences, healthcare-related manufacturing, etc. Prefer candidates from more apples-to-apples industries.
• May 29, 2026: All submissions must already be located in the Bay Area. No candidates who need to relocate. Candidates must be close enough to the Dogpatch SF office that 5 days/week on-site is realistic and sustainable.
• May 26, 2026: The hardest onsite round is a 2.5-hour AI agent-building build session under time pressure. Candidates comfortable with AI coding tools perform better. The team also wants candidates genuinely excited about a startup environment — strong technical candidates from larger companies sometimes lose momentum if hesitant about a smaller team.
• Sep 10, 2025: Salary band $150K–$350K — $250K cap for engineers with <5 years experience, $350K cap for 5+ years. Interviews are Python-heavy and move fast; best fits adapt in unstructured environments and move quickly.

The Role

A staff engineer to help shape the technical direction of Giga's backend systems — leading complex projects, making platform-wide architectural decisions, and raising the bar for engineering quality. This is a build role, not an advisory one: you write code, own systems, and ship, while also identifying what the team should build next and how.

What You'll Be Doing

• System architecture: Design how Giga's agent infrastructure evolves as it scales, making tradeoffs between speed, reliability, and complexity.
• Hard technical problems: Own the most ambiguous, highest-stakes projects where getting it wrong would hurt the business.
• Technical strategy: Identify gaps in systems before they become problems and build the case for addressing them.
• Engineering quality: Set standards for code review, system design, and operational excellence through your own work, and mentor other engineers via collaboration on real work (not formal management).

Tech stack: Python, Django, FastAPI, TypeScript, Node.js, AWS, Modal, Kubernetes, Postgres, Next.js

Qualifications

Seniority

• 7–15 years of experience in backend/infrastructure engineering, with Python and distributed systems [Required]

Work Experience

• Clear promotion trajectory (e.g. L4L6 in 6–7 years) [Must have]
• Experience at high-growth SaaS startups or top-tier tech (Meta, Netflix, Stripe, Snap, Uber preferred) [Required]
• Experience independently owning and driving initiatives to completion without oversight [Required]

Education

• CS, Math, or Stats degree from a top university globally [Strongly preferred]

Hard Skills

• System design expertise for scalable, reliable backend platforms [Required]
• Python backend development (Django/FastAPI) [Required]
• Uses AI coding tools (Cursor or Claude) actively [Strongly preferred]

Miscellaneous

• Must work on-site 5 days/week in San Francisco, and must already be located locally — not interested in candidates who need to relocate [Must have]
• Currently at or recently promoted to staff level (L6/E6 equivalent) [Required]

Traits to Avoid

• No slope: stagnant at the same level for 5+ years
• Pure architect/manager who no longer writes production code
• Legacy company lifers (Cisco, IBM, Oracle, banks, Salesforce)

Hard Blockers (confirmed disqualifiers)

• Healthcare tech — current or recent employer in healthcare tech at large (pharma, biosciences, healthcare-related manufacturing). (Client instruction, Jun 15, 2026.)
• Location / relocation — candidate must already be located within Bay Area driving distance of the Dogpatch SF office, sustainable for 5 days/week on-site. Willingness to relocate does not satisfy this. This overrides the standard rule that applying implies willingness to relocate. (Client instruction, May 29, 2026.)

Scoring note: Candidates are scored on full fit against all other requirements regardless of these blockers. The score reflects strength absent the blocker. Where a hard blocker applies, it is surfaced separately as HARD-BLOCKED FOR GIGA — [reason] so strong profiles remain visible for cross-submission to other roles. The block is never silently folded into the score.

Role Details

Salary$290K–$350KEquityCompetitive equityOn-site policy5 days in-office in San Francisco (Dogpatch)Visa sponsorshipAvailable — sponsorship and transfersEmployment typeFull-timeLocationSan Francisco, CA

Screening Questions

1. Are you able to work in San Francisco and come into the office 5 times per week?
2. Describe a complex backend system you've architected. What were the key trade-offs you made between speed, reliability, and complexity?
3. Tell us about a time you took a vague business problem and turned it into a concrete technical plan. What was the outcome?
4. Can the candidate be on-site? If not, is the candidate willing to relocate?
5. What is their salary expectation?
6. How actively is this candidate exploring new opportunities?

Interview Process

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

Stage 2 — Recruiter Screen (30 minutes) Initial conversation with a recruiter to discuss background, experience, and interest in the Staff Backend Engineer role at Giga.

Stage 3 — Technical Interview: System Design (1 hour) Architectural interview to assess ability to design complex, scalable backend systems and articulate technical tradeoffs.

Stage 4 — Final Round (3–4 hours) A series of interviews with key team members and leadership to evaluate team fit and alignment with Giga's engineering culture. Includes a ~2.5-hour AI agent-building exercise under time pressure.

Stage 5 — Offer Extended

Stage 6 — Candidate Hired

Ideal Companies & Backgrounds

Updated May 29, 2026

Ideal Companies Facebook, University of Waterloo, Meta

High-Slope Big Tech (Strong Infrastructure & Talent) Netflix, Stripe, Lyft, Snap Inc., Pinterest, Uber, DoorDash, Airbnb

High-Growth SaaS & Product-Led Startups Figma, Datadog, Snowflake, Notion, Brex, Databricks, Airtable, Miro, Rippling

AI-Native & Infrastructure Companies Cohere, Vercel, OpenAI, Modal Labs, Perplexity AI, Anthropic, Scale AI, Hugging Face

Non-Ideal Companies (avoid sourcing from)

Legacy Enterprise & Hardware IBM, HP, Dell Technologies, Oracle, Cisco, Salesforce, Broadcom, SAP, VMware

Large Tech with Perceived Talent Stagnation (unless high slope is evident) Microsoft, Google, Amazon, LinkedIn, Apple, Yahoo

Traditional Banking & Financial Services Capital One, Morgan Stanley, Goldman Sachs, Bank of America, JPMorgan Chase, Wells Fargo, American Express

Avoid Companies DFINITY Foundation, Stealth Startup, NVIDIA

Also avoid: healthcare tech at large (pharma, biosciences, healthcare-related manufacturing).

Ideal Candidate Profiles

For reference only — do not source these specific profiles.

Thien Nguyen — LinkedIn I craft stories in code and diagrams. | San Francisco Bay Area

• Meta + Snap experience — both preferred companies
• Sebastian could backdoor reference check via Snap network
• AI startup experience at Kardia Labs
• Area for improvement: if not producing code, not a fit
• Note: need to verify coding involvement

Austin Mease — LinkedIn Software Engineer focusing on generative AI and latency. Passionate string manipulator | San Francisco, United States

• Roblox Hippocratic AI trajectory shows strong backend + AI experience
• Staff-level at an AI startup — relevant domain

Jonathan Huang — LinkedIn Staff Software Engineer | San Francisco Bay Area

• High slope in big tech + experience at Netflix
• CS degree from Stanford

Rejected Candidate Feedback

• Sourcing candidates with a high slope and proven startup-style progression is critical — avoid those stagnant at large, slow-moving companies.
• Ensure candidates demonstrate strong hands-on coding and concrete production contributions, not just managerial or architect roles.
• Reconfirm candidates are locally based in SF or fully committed to on-site work, and can articulate clear trade-offs in their system designs.