About Sapience AI

Sapience AI is the collective intelligence platform for professional communities. We sit above the CRMs, AMS platforms, and knowledge bases that organizations already run, and we turn the expertise scattered across them into something every member can search, act on, and share.

The intelligence a community needs is already inside it. Most organizations just cannot reach it. Knowledge lives in silos, in legacy systems, in the heads of a few experts, and in fragmented records no one can connect. We change that.

Our work is grounded in four commitments: technology elevates people and never replaces them, the best expertise is already inside the community, everything is built on trust, and every deployment is purpose-driven for the organization it serves.

Let’s achieve more, together.

Where this role sits

This is an end-to-end engineering role that ships complete, working experiences in the customer’s world. You build across the stack, from the data and services underneath to the interface a member touches.

You take MINERVA and the COGENT architecture and turn them into a solution a specific community can use: the workflows, the screens, and the connections that make collective intelligence part of how they work every day.

You move at the speed of a deployment and own the whole path from problem to a running product members actually adopt.

Why this role exists

A platform only creates value when people use it. For each community, that means an experience shaped around how they work, connected to their systems, and easy enough that expertise flows without friction.

Building that experience end to end, fast, in a customer environment, takes an engineer who can move from data pipeline to interface without a handoff at every seam.

The Forward Deployed AI Full Stack Engineer is that person. You deliver the whole solution, get it adopted, and carry what you learn back so the core platform serves the next community better.

What you will own (Areas of Responsibility)

You hold seven areas of responsibility inside the forward deployed team. Each one is yours to set direction on, build, and measure.

1. End-to-end solution delivery

• Build complete solutions for customer deployments, from backend services to the member-facing interface.
• Own the full path from problem to a running experience in the customer’s environment.
• Ship quickly under deployment timelines while keeping the result maintainable.

2. Member-facing experiences on MINERVA

• Build the interfaces and workflows that let members search, act on, and share their community’s expertise.
• Design for adoption: fast time-to-value and low friction, so people actually use what you build.
• Partner with product design to make the experience clear, trustworthy, and worth returning to.

3. Backend, data, and COGENT integration

• Build the services, pipelines, and integrations that connect a deployment to the customer’s systems and the KO graph.
• Integrate deployment logic with the COGENT architecture’s reasoning and retrieval.
• Keep the data behind the experience current, accurate, and searchable.

4. Trust, security, and governance in the experience

• Build access, permissions, and data handling so members only ever see what they should.
• Make governance and provenance visible in the experience, so people trust what the platform tells them.
• Meet the customer’s security and privacy requirements across the whole stack.

5. Adoption instrumentation

• Instrument the experience to show whether members are adopting it and reaching value.
• Use those signals to improve the deployment after launch, not just before it.
• Feed adoption learnings back to product and the field.

6. Reusable components and field leverage

• Turn repeated deployment work into reusable components, templates, and patterns.
• Contribute improvements back into the core platform so the next deployment starts further ahead.
• Keep the field team’s shared toolkit current as the platform moves through each GR/GA milestone.

7. Collaboration and handoff

• Work alongside forward deployed sales and backend engineers to deliver complete solutions.
• Leave the customer’s team able to run and extend the experience.
• Hand off cleanly to customer success with the documentation they need.

AI-augmented ways of working

You build with AI across the stack. You use AI coding assistants and Sapience AI’s own systems to accelerate delivery, prototype interfaces, and reason through unfamiliar code, while you own the architecture, the experience, and the quality of what ships.

The standard is human in partnership: AI accelerates the work, you own the judgment, the interpretation, and the call. The people who create the most value here are not the ones producing the most output. They are the ones turning evidence into clear, durable decisions.

What this role is not

To keep the boundary clear:

• This is not a core platform-only role. You build in customer environments and feed patterns back, rather than working solely on the central codebase.
• This is not a design-only or backend-only role. You own the whole stack, from data to interface.
• This is not a research role. You apply and deploy AI in production rather than developing new model architectures.
• This is not a throwaway-prototype role. You ship experiences members adopt and others can maintain.

What success looks like

We measure this role on outcomes the team can see:

• Adopted experiences. Members use what you build, and they reach value quickly.
• Complete solutions. You deliver end to end without dropping the seams between layers.
• Speed with quality. Deployments ship fast and remain maintainable and protected.
• Reusable leverage. Custom work becomes components that speed up the next deployment.
• Trust in the product. Permissions, provenance, and governance are visible and correct.
• Platform improvement. Your contributions make the core serve future communities better.

Who you are

Required qualifications

• Five or more years building full stack applications in production.
• Strong frontend skills with a modern framework, plus solid backend and data engineering.
• Hands-on experience building with modern AI and ML systems, including LLMs and retrieval.
• A track record of shipping complete, adopted products under real deadlines.
• Comfort integrating third-party systems and APIs.
• Care for security, permissions, and data protection across the stack.
• Strong product instincts and attention to the member experience.

Preferred qualifications

• Experience deploying software at customer sites in a forward deployed model.
• Familiarity with retrieval-augmented generation, vector search, and knowledge graphs.
• Experience integrating CRM, AMS, or knowledge-platform systems.
• Design sensibility and comfort collaborating closely with product design.
• Exposure to agentic or neuro-symbolic AI systems.

How you work

• You lead with the member’s real need and name the problem before the solution.
• You move fast across the whole stack and keep quality where it counts.
• You care about adoption, not just delivery.
• You treat trust and data protection as design constraints.
• You share reusable work so the whole team moves faster.

Skills & Competencies

• Full stack development across frontend, backend, and data.
• Building member-facing AI experiences that people adopt.
• Retrieval, embeddings, and grounding AI behavior in trusted data.
• Integration across enterprise systems, identity, and permissions.
• Adoption instrumentation and iterating on live experiences.
• Secure-by-design engineering for sensitive community data.
• Turning custom deployment work into reusable components.

Services & Tools Experience

• TypeScript and JavaScript with a modern frontend framework (React or similar).
• Python or Node for backend services.
• LLM orchestration and retrieval frameworks, plus vector and graph databases.
• REST and GraphQL APIs and common integration patterns.
• Cloud platforms (AWS, GCP, or Azure) and containers.
• SQL and NoSQL data stores, plus analytics and instrumentation tooling.
• CRM, AMS, and knowledge-base APIs, and Sapience AI’s MINERVA and COGENT interfaces (trained on the job).

Prior Experience & Background

• Prior full stack engineering at a software or AI company, ideally including customer-facing delivery.
• Experience owning products end to end, from data to interface.
• A track record of shipping experiences that people actually adopt.
• Experience mentoring or setting patterns for other engineers is a plus.

Cross-functional partners

You work most closely with the forward deployed sales and backend engineers, Applied AI, Product Design, and the customer’s own teams. You partner with core platform teams to feed reusable patterns back into MINERVA and COGENT.

How we hire

We review every application, and we encourage you to apply even if you do not match every line above. Research shows that talented people, especially those from underrepresented communities, often hold back when they do not meet every qualification. If that is the only thing holding you back, apply anyway.

Sapience AI is an equal opportunity employer. We are committed to a workplace where everyone, regardless of background, has a voice in building what comes next.

Compensation

Base Salary: $156,000 - $168,000 + early equity.

Generous health and wellness benefits

Sapience AI is an equal opportunity employer. We do not discriminate on the basis of gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic. If you need an accommodation to complete our application process, let your recruiter know.