Founding Product Engineer (AI/ML) - London

Salary: £130,000 – £150,000

Equity: 0.7% - 1%

In-person, with flexible remote days

European visa sponsorship available

About Aurora

Aurora is a digital-first lifestyle management platform built for the world’s highest-performing individuals and organizations. Our members include tier-1 founders, executives, athletes, and families who delegate complex, ongoing needs across travel, dining, experiences, luxury purchases, and health.

Aurora is mobile-first, conversational, proactive, and available 24/7 globally. Members interact with the product multiple times per day, often for time-sensitive, high-stakes decisions. This makes product quality, judgment, and system behavior directly visible to customers in real time.

Agentified premium lifestyle commerce represents a $10bn+ opportunity. Legacy concierge and lifestyle services rely on fragmented CRMs and manual workflows that do not scale. Aurora combines automation, AI agents, and expert human curators into a single system designed for speed, accuracy, and leverage.

We’ve seen exceptional early traction:

• 51% DAU, 95% WAU
• Transaction volume and member spend already outperforming industry benchmarks by 5× within the first three months

Aurora is backed and chaired by Alex Macdonald, the most successful founder in this category.

So far, we’ve:

• Raised >$5M from experienced operators and tier-1 investors
• Built a product with industry-leading engagement from paying members
• Collected a large, proprietary dataset of user context (preferences, calendars, health signals) that powers proactive AI behavior

We are now expanding the founding engineering team as we scale the product and the member base.

Aurora is built in person from our offices in London, New York, and Los Angeles.

The engineering team works primarily from our London office in Belgravia, with flexible remote days.

The role

We’re hiring a Founding Product Engineer to own and ship user-facing, AI-driven features end to end.

This is a hybrid role that sits between engineering and product. You will be hands‑on technically, while also acting as the product owner for specific features. You’ll define problems, shape solutions, work directly with curators and stakeholders, and ensure what we ship actually delivers value to members.

You’ll work closely with the CTO, founders, backend and mobile engineers, and the operations team. Many of Aurora’s most important features live at the intersection of product judgment, ML systems, and agent orchestration - this role owns that intersection.

This is a high‑ownership role. You will take ambiguous ideas, turn them into concrete systems, ship them to production, and iterate based on real customer usage and feedback.

Over time, this role can evolve into senior product leadership, technical leadership, or a hybrid path, depending on your strengths and ambitions.

Aurora attracts people who want to build their own companies. We treat this role as training for that level of ownership.

What you’ll do

• Own product features end-to-end, from problem definition to production, feedback, and iteration
• Act as product owner for assigned areas, balancing customer needs, technical constraints, and delivery speed
• Design and implement AI-powered workflows using LLMs, tools, and agentic systems
• Build and iterate on ML-driven features, including classification, ranking, extraction, and decision support
• Design agent flows that combine automation with human‑in‑the‑loop escalation
• Work closely with our internal domain experts to understand real operational pain points and opportunities for leverage
• Collaborate with engineers to ship reliable, scalable implementations
• Define success metrics and use qualitative and quantitative feedback to guide iteration
• Debug production issues related to AI behavior, data quality, or workflow design
• Contribute code across the stack where needed, with a bias toward shipping value

What we’re looking for

Experience

• 4+ years of experience building production software
• Proven experience shipping ML- or AI-powered features used by real customers, ideally at scale
• Experience working with LLMs, prompt design, tool calling, and agentic flows
• Strong understanding of how ML systems behave in production, including failure modes

Technical foundation

• Comfortable writing production-ready Python code
• Solid understanding of backend systems and APIs, even if not infrastructure-focused
• Experience working with data pipelines, embeddings, retrieval, or RAG-style systems
• Ability to reason about latency, cost, reliability, and quality trade-offs

Product mindset

• You naturally think in terms of user outcomes, not just implementation details
• Comfortable operating with ambiguity and incomplete information
• Able to define the “right” problem before jumping to solutions
• Willing to make pragmatic decisions and adjust quickly based on feedback

Collaboration

• Strong communicator who can work across engineering, product, and operat