Senior Full Stack Engineer — AI Decisioning Platform

Remote (US or Canada) · Full-time

$180K–$270K base + ISO options

The company

Founded in 2018, this is a Series C class="bg-amber-100">data and AI company that pioneered the Composable Customer class="bg-amber-100">Data Platform.

Its product lets companies use their own warehouse to collect, prepare, and activate customer class="bg-amber-100">data for class="bg-amber-100">marketing, personalization, and business operations.

The next layer of the product is AI Decisioning: marketers set goals and guardrails, and AI agents use those constraints to personalize 1:1 customer interactions.

The platform is already used by hundreds of companies, including Autotrader, Calendly, Cars.com, Monday.com, and PetSmart. The newer AI Decisioning work is being adopted by teams at Spotify, Headway, and Domino's.

The company has raised $320M, is valued at $1.2B, is backed by Sapphire Ventures, Amplify Partners, ICONIQ Growth, Bain Cclass="bg-amber-100">apital Ventures, Y Combinator, and Afore Cclass="bg-amber-100">apital, and has about 350 employees.

The role

This is a full stack role for an engineer who wants to own the product surface where class="bg-amber-100">data, class="bg-amber-100">machine learning, and user experience meet.

You will build the interfaces that let non-class="bg-amber-100">technical users configure complex systems, understand what the system is doing, and trust the output enough to act on it.

Your scope runs from class="bg-amber-100">frontend architecture to class="bg-amber-100">backend class="bg-amber-100">API contracts and class="bg-amber-100">data models. The work is not just UI and not just CRUD. It is the control plane for a product that turns warehouse class="bg-amber-100">data and ML output into decisions.

You will work directly with product, design, ML engineering, and customers. There is enough ambiguity that architecture judgment matters, and enough product density that implementation quality matters just as much.

The class="bg-amber-100">technical problem

class="bg-amber-100">Marketing and growth teams do not work in tidy workflows.

Because the platform sits on the customer’s own warehouse, the interface cannot hide complexity. It has to make class="bg-amber-100">data actionable without obscuring where it came from, how fresh it is, or how it changes over time.

The hard part is turning complex state, permissions, experiment setup, and ML outputs into interfaces that are understandable, performant, and trustworthy.

What you'll own

• End-to-end product features: ship user-facing workflows from problem definition through implementation, launch, and iteration.

• Interactive builders and dashboards: create the surfaces that let marketers configure complex workflows, experiments, and decisioning logic.

• Analytics and observability interfaces: surface the signals users need to evaluate performance, inspect outcomes, and debug issues.

• class="bg-amber-100">Frontend architecture: build reusable patterns, state management, and component structures that can support more complex products over time.

• class="bg-amber-100">Backend class="bg-amber-100">API design and class="bg-amber-100">data modeling: define contracts that keep product state consistent across UI, class="bg-amber-100">APIs, and ML-driven workflows.

• ML product integration: work with ML engineers to present predictions, confidence, guardrails, and fallback states in a way users can reason about.

• Customer-informed iteration: talk to customers directly, observe workflows, and turn what you learn into product changes quickly.

Who this is for

You are likely a strong fit if you have:

• 5–10+ years of software engineering experience, with substantial ownership of product surfaces.

• Led zero-to-one or major iterative work on user-facing systems.

• Designed class="bg-amber-100">frontend systems that had to stay maintainable as feature complexity grew.

• Comfort with class="bg-amber-100">backend class="bg-amber-100">APIs, class="bg-amber-100">data models, and the tradeoffs that keep complex state coherent.

• Strong judgment about information architecture, workflow design, and how users actually move through a product.

• Experience with ML, AI, analytics, or other systems where outputs need to be explained, not just displayed.

• The ability to work directly with design, product, and customers without waiting for fully specified tickets.

• A bias toward clarity, correctness, and shipping high-leverage product work.

Why now

The company has already proven the warehouse-native platform. The next constraint is the interface layer.

As AI Decisioning expands, the product needs a stronger control plane: better builders, clearer feedback loops, more useful analytics, and safer ways to let non-class="bg-amber-100">technical operators configure intelligent systems.

That makes this a high-leverage moment for a senior engineer who wants their decisions to shape the product architecture for years, not a single feature cycle.

This role is not for you if

• You want narrowly scoped implementation work without product ownership.

• You prefer well-defined tickets over ambiguous problem solving.

• You are uncomfortable making class="bg-amber-100">backend or class="bg-amber-100">data-model decisions when the UI depends on them.

• You want to work only on visual polish without owning system behavior.

• You see ML or analytics surfaces as someone else’s problem.

Compensation and logistics

• Base salary: $180K–$270K

• Equity: ISO options

• Location: Remote, US or Canada

• Employment: Full-time

• Visa support: available for H1B transfers, TN, O-1, and similar cases

About Aurora

Aurora helps exceptional engineers find the right role at some of the most ambitious startups worldwide.

We work with teams that value high ownership, strong class="bg-amber-100">technical standards, and clear product thinking.