Senior Data and Machine Learning Engineer

ABOUT US

Indie Campers is the leading campervan provider, dedicated to making road trips accessible and unforgettable for everyone. Innovation, product-led growth, and an unwavering commitment to our customers are at the heart of everything we do. With more than one million nights rented and travellers from 169 countries, we provide a single and trustworthy digital experience for road trips in the United States, Europe and Oceania, with different campervan configuration options and even the chance to buy one of our vehicles.

Our ambitions are big, and so are the challenges we embrace. We are scaling our technology organisation to unlock the next wave of automation- and AI-powered experiences that will delight customers and empower employees worldwide.

THE ROLE

You’ll build the pipelines, features, and services that power ML and analytics at Indie Campers, splitting work across data engineering (batch/stream) and MLOps (training/serving/monitoring).

WHAT WILL YOU WORK ON?

• Build APIs/microservices (Go/Python) and thin UIs (React/Next).
• Integrate LLMs (OpenAI/Claude), embeddings, retrieval, and tool use.
• Implement evals, guardrails, and observability for AI features.
• Set up basic MLOps: model/embedding stores, vector DBs, batch/online flows.
• Ship rapidly with experiments, metrics, and cost controls.

WHO ARE WE LOOKING FOR?

• 5–8+ years data eng/ML infra; Python, SQL, dbt.
• Orchestration, streaming, columnar storage; CI/CD for data.
• MLOps stack (MLflow, Feast/Tecton, SageMaker/Vertex).
• Pragmatic, reliable, collaborative.

CORE VALUES AND OPERATING PRINCIPLES

• Customer First: Relentless in providing the best service at the best price to all customers.
• Ownership: Proactively take ownership and deliver on our goals.
• Committed: Work together, as a team, for the long-term.
• Learn, build and structure.
• Be precise and go deep.

Are you ready to Go Indie?

KPIS & TARGETS (YEAR 1)

• Pipeline success ≥ 99.5%; freshness SLA met ≥ 95%.
• 2–3 models to production with monitored lift; rollback < 30 min.
• New source onboarding ≤ 10 days median.