AI Engineer

Aifund; Mountain View (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

Aifund is searching for an AI Engineer to create full-stack prototypes that pressure-test innovative ideas. You'll collaborate closely with product designers and AI experts, ensuring the architecture aligns with user behavior and constraints.

The ideal candidate has over three years of software engineering experience, particularly in AI applications, and excels in both front-end and back-end development. This role requires strong communication skills and a passion for pushing the boundaries of AI technology.

Qualifications

• 3+ years of software engineering experience, including ownership of at least one production AI application.
• Demonstrated experience building applications with modern AI capabilities.
• Strong communication and collaboration skills across disciplines.

Responsibilities

• Build full-stack AI prototypes to pressure-test venture ideas.
• Collaborate with product and AI experts to create and iterate on concepts.
• Direct frontier coding agents to turn technical intent into software.

Skills

Software engineering experience

AI application architecture

Large language models experience

Front-end and back-end fluency

SQL and NoSQL systems

Strong communication skills

Tools

APIs

Cloud deployment

Data modeling

Job description

Who We Are

AI is the new electricity: Just as electricity transformed numerous industries starting 100 years ago, AI is now poised to do the same.

AI Fund is a venture studio founded by Dr. Andrew Ng in 2017. Our portfolio companies use AI technology to build applications across numerous industry sectors. The AI Fund team combines their experiences as AI pioneers, entrepreneurs, venture capitalists, investors, and operators. We are backed by a $390-million dollar fund from top-tier global corporations and VC firms.

Our purpose is to build AI companies that move humanity forward

What We're Looking For

We are seeking an AI engineer with architecture judgment, product instincts, and fluency across the modern AI application stack.

You should be able to decompose AI products into composable building blocks: model selection, prompting, tool use, retrieval, structured outputs, memory and state, workflow orchestration, planning, reflection, evaluation, observability, and safety controls. You should understand the reason each component belongs in a system, the tradeoffs it introduces, and the evidence needed to know whether it is working.

You start with the simplest design that can answer the open question about an idea, and tighten the architecture only as evidence justifies it.

You follow where AI is heading and ground engineering decisions in product goals, user behavior, the data available for the problem, system constraints, and measured performance. You will collaborate closely with product thinkers, designers, AI experts, and engineers to validate or falsify venture ideas through functional prototypes.

Responsibilities

• Build full-stack AI prototypes that pressure-test venture ideas before founder or entrepreneur-in-residence handoff.


• Design AI systems from composable building blocks and make the tradeoffs visible to product and engineering partners.


• Choose retrieval and context strategies that fit the data and task, from structured queries and hybrid search to reranking, graph traversal, and long-context or human-curated context.


• Build agentic and workflow-based systems with clear control flow, bounded autonomy, useful tool interfaces, state management, recovery paths, and human review where appropriate.


• Make architecture and platform choices that fit the stage of an idea, keeping prototypes cheap to change while leaving a credible path to production if the idea validates.


• Build and integrate APIs, databases, third-party services, internal tools, and cloud infrastructure.


• Define evaluation loops for AI behavior, including task success, retrieval quality, factuality, tool-call correctness, grounding, safety, latency, cost, and user-perceived quality.


• Use error analysis to decide whether to improve prompts, data, retrieval, tools, orchestration, model choice, UX, or product scope.


• Collaborate cross-functionally with product, design, and AI experts to create, test, and iterate on new concepts using direct user feedback.


• Present build results to potential entrepreneurs-in-residence and founders: what worked, what failed, what they need to know to decide next steps.


• Direct frontier coding agents to turn clear product and technical intent into working software, while owning the architecture, review, debugging, and quality bar.


• Identify and troubleshoot issues across the full stack, including frontend, backend, AI orchestration, data pipelines, deployment, and production behavior.


• Contribute to better development processes, reusable engineering practices, and shared technical judgment across the team.

Qualifications

• 3+ years of software engineering experience, including end-to-end ownership of at least one production AI application architecture spanning UI, backend, data, models, tools, and evaluation.


• Demonstrated experience building applications that use large language models, multimodal models, or other modern AI capabilities in product workflows.


• Strong technical fluency across frontend, backend, APIs, databases, and cloud deployment, with enough depth to review, debug, and steer implementation.


• Expert ability to work with frontier coding agents, including writing precise specs, decomposing work, inspecting generated code, catching architectural mistakes, and deciding when to intervene directly.


• Ability to justify retrieval choices against corpus structure, freshness, permissions, latency, precision, recall, and cost.


• Experience with SQL and NoSQL data systems, including the ability to model data for application use, retrieval, analytics, and operational reliability.


• Strong communication skills and the ability to work collaboratively across disciplines.


• Habit of reading papers, model cards, technical postmortems, and production writeups, then folding useful lessons into the next build.

Additional Experience

• Experience shipping MVPs, prototypes, or early-stage products under ambiguity.


• Experience as a technical lead, architect, founding engineer, or senior builder on AI-driven products.


• Contributions to open-source AI, developer tools, evals, retrieval, agents, or applied ML infrastructure.


• Interest or experience in product design, product strategy, or company creation.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.