Recruiting Partner Notes- Not to be shared with candidates
Please review the following requirements carefully before submitting a candidate.
This is a highly selective, fully onsite Full Stack AI Engineer search for an early-stage startup in San Francisco. The client is specifically looking for engineers with genuine zero-to-one startup experience.
Candidate Requirements
Candidate must currently live within a reasonable commuting distance of San Francisco and be willing to work onsite five days per week.
Remote, hybrid, and relocation-dependent candidates will not be considered.
Candidate must have prior experience working at an early-stage startup.
Candidate must have personally helped build and launch a product from zero to one.
Please do not submit candidates whose experience is exclusively with FAANG, large technology companies, consulting firms, or mature enterprise environments.
FAANG experience is acceptable when combined with meaningful early-stage startup experience.
Candidate must still be hands-on and actively writing production code. This is not an engineering management or architecture-only position.
Candidate should have strong full-stack experience across frontend, backend, databases, APIs, cloud infrastructure, and production deployments.
Candidate must have practical experience building AI-powered products. API experimentation, personal projects, coursework, or basic chatbot integrations alone will not qualify.
Strong candidates will have experience with AI agents, LLM integrations, RAG, model tool calling, multimodal AI, voice AI, evaluations, or real-time AI systems.
Resume and LinkedIn Verification
Before submitting, carefully compare the candidate's resume and LinkedIn profile.
Employment dates, job titles, company names, education, and career history should be consistent.
Please resolve any discrepancies before submitting the candidate.
Do not submit candidates with unexplained overlapping employment, conflicting dates, inflated titles, or companies that cannot be reasonably verified.
Confirm that the candidate personally performed the work being presented and is not overstating team accomplishments as individual contributions.
A current LinkedIn profile must be included with every submission.
Please discuss any employment gaps, short tenures, or major career transitions with the candidate before submitting.
Required Candidate Screening
Please speak directly with the candidate before submitting. Do not submit candidates based only on a resume review, email exchange, or LinkedIn message.
Your candidate summary should address:
Current location and ability to work onsite five days per week
Current compensation and compensation expectations
Work authorization and any present or future sponsorship requirements
Early-stage startup experience
Specific zero-to-one product the candidate helped build
The candidate's personal technical contribution to that product
Production AI experience and the AI technologies used
Current level of hands-on coding
Reason for leaving each recent position
Interest in joining a fast-moving, early-stage startup
Interview availability and potential start date
Submission Quality
Please submit only candidates who clearly meet the core requirements. Quality is significantly more important than volume.
Do not submit candidates who have not reviewed the onsite requirement.
Do not submit candidates who are applying broadly without a specific interest in this opportunity.
Do not submit candidates who are primarily seeking management, remote work, or a highly structured corporate environment.
Do not submit candidates who need extensive convincing regarding compensation, location, startup risk, or onsite expectations.
Confirm the candidate has authorized you to represent them for this specific opportunity.
Check Top Echelon for duplicate ownership before submitting.
Include a concise candidate write-up explaining why the individual is a strong match. A resume without screening notes will not be considered a complete submission.
Interview Readiness
Candidates should be prepared to discuss:
A product they personally built from zero to one
Technical and architectural decisions they made
Tradeoffs between speed, scalability, cost, and quality
AI systems they have deployed into production
How they measured and improved AI output quality
A situation where requirements were unclear and they had to create the solution
Their individual contribution versus the contribution of the broader team
Why they want to work onsite at an early-stage San Francisco startup
Candidates may be asked to complete a technical assessment, live coding exercise, architecture discussion, or product-building exercise.
JOB DESCRIPTION:
We are partnering with an early-stage, cutting-edge AI startup in San Francisco to hire a Full Stack AI Engineer with strong zero-to-one product-building experience.
This is a fully onsite position. Candidates must currently live in the San Francisco Bay Area and be able to work from the company's San Francisco office five days per week. Please do not apply if you are seeking remote or hybrid work.
About the Role
Our client is looking for a highly hands-on engineer who can help build and launch AI-native products from the ground up. You will work closely with the founders to turn ideas into production-ready applications and help shape the company's technical foundation.
The right candidate is comfortable working across frontend, backend, infrastructure, and AI systems. You should enjoy fast-moving startup environments, unclear requirements, rapid iteration, and taking full ownership of what you build.
Responsibilities
Build, test, launch, and improve full-stack products from zero to one
Develop modern frontend applications using React, Next.js, or similar technologies
Build backend services, APIs, databases, and integrations
Create AI-powered features using large language models and multimodal models
Build AI agents that can use tools, complete workflows, and interact with external systems
Develop retrieval-augmented generation, embeddings, vector search, and knowledge systems
Integrate voice AI, speech-to-text, text-to-speech, or real-time conversational systems
Implement model routing, structured outputs, tool calling, and human approval workflows
Build evaluation, monitoring, and testing systems for AI performance
Improve application speed, reliability, scalability, security, and AI inference costs
Work directly with founders, users, and product stakeholders
Help make product, architecture, and technology decisions
Qualifications
4 or more years of professional software engineering experience
Proven experience building and launching a product from zero to one
Strong experience across both frontend and backend development
Experience working at an early-stage startup
Strong skills in TypeScript, JavaScript, React, and Next.js
Backend experience with Node.js, Python, FastAPI, Go, or similar technologies
Experience with PostgreSQL, APIs, cloud infrastructure, and production deployments
Hands-on experience integrating large language models into production applications
Experience with AI agents, RAG, tool calling, embeddings, or multimodal AI
Strong product judgment and the ability to work with limited direction
Ability to move quickly while maintaining high engineering standards
Preferred Experience
OpenAI, Anthropic, Gemini, or open-source AI models
Model Context Protocol and agent orchestration frameworks
LangGraph, LlamaIndex, Vercel AI SDK, or similar tools
Vector databases such as Pinecone, Qdrant, Weaviate, or pgvector
Voice AI, WebSockets, WebRTC, or real-time streaming systems
AI evaluations, observability, prompt injection protection, and model security
Docker, AWS, Google Cloud, Azure, or serverless infrastructure
Experience as an early, founding, or first engineering hire
Ideal Candidate
You are a builder who prefers creating new products over maintaining mature systems. You are comfortable wearing multiple hats, making decisions quickly, and turning incomplete ideas into products that customers can use.
You understand that strong AI products require more than connecting to a model API. You know how to build dependable workflows, evaluate output quality, manage latency and cost, and create safeguards around AI-generated actions.
Location Requirement
This role is onsite in San Francisco five days per week.
Candidates must already live within a reasonable commuting distance of San Francisco. Remote, hybrid, relocation-dependent, and out-of-area candidates will not be considered.