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EatClub is Australia's #1 Food & Drink app, helping over 4 million customers discover restaurants and access real-time deals (up to 50% off), while empowering 8,000+ venues to fill tables and maximize revenue. Named Australia's Fastest Growing Tech Company by the AFR in 2025, the company is a 150+ person scaleup co-founded by Marco Pierre White and food tech leaders.
As Senior Machine Learning Engineer, you will own the ML infrastructure that powers demand forecasting, affinity modelling, and restaurant grouping across thousands of venues. Your primary focus is translating data science requirements into production systems: the feature store, model deployment pipelines, Databricks-based experimentation environment, and real-time serving layer for restaurant operators.
Day-to-day responsibilities include:
- Building and hardening the feature store (online/offline consistency, freshness, backfills) that the data science team relies on by default
- Owning the model deployment pipeline end-to-end: versioning, rollout, rollback, monitoring, and drift detection
- Designing and operating serving APIs that turn forecasts into actionable insights for venue operators
- Building the Databricks experimentation environment in partnership with the Senior Data Scientist
- Productionizing new modelling approaches (e.g., neural forecasters): benchmarking compute, latency, and cost
- Developing infrastructure for variance attribution and LLM/vector-DB based forecast explainability
- Implementing low-latency serving for hourly/intraday demand on top of daily forecasts
- Building the Actions Feed intelligence layer: pipelines that turn forecast deltas into ranked, executable recommendations
- Developing forecast confidence infrastructure: serving quantile bands and calibrated confidence scores at scale
- Advancing the team's AI-first workflow: agentic loops, async runs, human-in-the-loop review
You will work closely with the Senior Data Scientist as a technical peer, translating modelling requirements into system design. You'll also collaborate with backend engineers on POS data pipelines, and with the Product Manager on reliability priorities and platform roadmap.
Key success metrics: feature store becomes the default infrastructure other teams build on; models ship from notebook to production in days, not weeks; serving layer is reliable enough operators trust the numbers implicitly; variance attribution and Conversational Venue Assistant go live with grounded reasoning.
REQUIREMENTS:
- Exceptional communication skills, especially translating modelling requirements into system design
- Strong Python and production software engineering fluency (typed code, testing, CI/CD, code review)
- Deep MLOps experience as primary strength: model versioning, deployment pipelines, workflow orchestration, experiment tracking, model serving, monitoring, drift detection (shipped end-to-end)
- Solid hands-on experience with Databricks or equivalent platform for experimentation and production
- Feature store design and implementation experience (online/offline consistency, freshness, backfills)
- Strong grasp of AWS services relevant to ML infrastructure (compute, storage, orchestration, serving)
- API design and backend engineering capability: can own a serving layer, not just consume one
- Forecasting/ML literacy to be a genuine technical peer to data scientists: understanding quantile loss, exogenous regressors, time-series cross-validation well enough to design systems around them
- Strong bias to action and shipping evidence (systems over RFCs)
- AI-first working style: Claude Code, agentic workflows, AI integrated into daily workflow
NICE-TO-HAVE:
- LLM, RAG, or vector-DB infrastructure experience
- Experience building or scaling a feature store from scratch
- Hospitality, retail, demand-forecasting, or marketplace domain experience
- E-shaped generalist breadth: ML engineering + data engineering + data science + analytics + software engineering
- Experience setting up ML platforms or pairing with strong data scientists without territorial dynamics
CULTURAL FIT:
- Default to shortest path to measured results in production
- Comfortable working alongside strong Data Scientist as peer (not hierarchically above or below)
- Direct, low-ego, willing to be wrong in public
- Curious about the actual problem (restaurant operators making better decisions), not just infrastructure
- Treat AI tools as leverage, not novelty
