Senior Machine Learning Engineer

About this role

About EatClub
At EatClub, we believe restaurants and bars are the beating heart of every city’s culture. Whether it's discovering a hidden gem, grabbing a late-night takeaway, or meeting friends for a drink, our mission is simple: help the hospitality industry thrive through smart, powerful tech.

Our platform helps over 4 million customers discover top restaurants and access real-time deals that save them up to 50% off the bill. We empower more than 8,000 venues to fill empty tables, increase foot traffic, and maximise revenue.

#1 app in Food & Drink and awarded Australia's Fastest Growing Tech Company by the AFR in 2025. Now is an exciting time to join our team. Initially co-founded by Marco Pierre White and leaders in the food tech scene, we're now a 150+ person scaleup that's growing fast and making waves in the industry.

Why You’ll Love Working With Us

• Be part of an innovative company shaping the future of dining

• Autonomy, flexibility, and a collaborative culture

• A passionate team who values creativity, hustle and results

• Access to some of the best restaurants and hospitality leaders in the industry

A Day-in-a-Life of our Senior Machine Learning Engineer
You will spend your days deep in infrastructure work - the feature store, model deployment pipelines, the Databricks-based experimentation environment, and the serving layer that puts predictions in front of restaurant operators in real time. You will collaborate closely with the Senior Data Scientist to translate modelling requirements into production systems: what the feature store needs to serve, how models get versioned and rolled out, how experiments get tracked and compared. You'll leverage AI tooling - agentic coding workflows, AutoML integration, LLM-assisted debugging - to expedite build cycles and keep the platform lean.
There's ambiguity. There's speed. There's ownership.

You will work closely with the Senior Data Scientist to turn modelling requirements into deployable systems - defining the contract between feature engineering and feature serving, between model training and model deployment. With backend engineers, you will own POS data pipelines and the serving APIs that sit downstream of them. With the Product Manager, you will have a conversation: what needs to be reliable today, what can be rebuilt tomorrow, and where the platform should flex for what's coming next. Occasionally, the BD lead will pull you into a session with real restaurant operators - the moments where you see latency, staleness, or a broken pipeline land as a bad decision in an actual venue.

On any given week, you will

• Stand up or harden a piece of the feature store, and make it the thing the Senior Data Scientist reaches for by default

• Own the model deployment pipeline end to end: versioning, rollout, rollback, monitoring, drift detection

• Build and maintain the Databricks-based experimentation environment, in partnership with the Senior Data Scientist

• Design and operate the serving APIs that turn a forecast into something a restaurant operator sees in the product

• Productionise a new modelling approach (e.g. a TiDE-class neural forecaster) - benchmark compute footprint, latency, and cost before it ships

• Build the infrastructure behind variance attribution and the LLM-and-vector-DB direction for "why did this prediction change"

• Push the team's AI-first workflow forward: agentic loops, async runs, humans on final review

• Work on hard systems problems and review your work with the Senior Data Scientist

Type of projects you'll be working on at EatClub

• The feature store and model deployment pipelines that serve demand forecasting, affinity modelling, and restaurant grouping across thousands of venues

• Building and owning the refined experimentation environment (Databricks) that the whole data science function runs on

• Serving infrastructure for per-venue model selection: routing the right architecture to the right venue cohort in production, reliably

• The retrieval and vector-DB infrastructure behind variance attribution and forecast explainability

• Low-latency infrastructure for hourly / intraday demand serving on top of the daily forecast

• The Actions Feed intelligence layer: the pipelines that turn forecast deltas into ranked, executable recommendations, on time, every time

• Infrastructure for forecast confidence: serving quantile bands (P10 / P90) and calibrated per-day confidence scores at production scale

You have

• Exceptional communication skills, specifically for translating modelling requirements into system design with a data scientist as your closest partner

• Strong Python and production software engineering fluency (typed code, testing, CI/CD, code review discipline)

• Deep MLOps experience as your primary strength: model versioning, deployment pipelines, workflow orchestration, experiment tracking, model serving, monitoring, and drift detection, shipped end to end

• Solid hands-on experience with Databricks, or an equivalent platform, as an experimentation and production environment

• 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 chops: you can own a serving layer, not just consume one

• Enough forecasting/ML literacy to be a genuine technical peer to a data scientist - you don't need to build the models, but you need to understand quantile loss, exogenous regressors, and time-series cross-validation well enough to design systems around them

• Strong "bias to action" and shipping evidence (not RFCs, shipped systems)

• "AI-first" working style: Claude Code, agentic workflows, AI in your daily loop

It would be extra awesome if you also had

• LLM, RAG, or vector-DB infrastructure experience (we have a real use case in variance attribution and the Conversational Venue…