Role : Senior Machine Learning Engineer

Who We're Looking For :

We're not hiring a coder. We're hiring an engineer. We want someone who genuinely loves solving problems, thinks beyond the blocker in front of them, and reasons about the whole system rather than just the file they happen to be editing.

If your instinct when hitting a wall is to find a way around, over, or through it - and then tell the team what you decided and why - keep reading.

What You'll Do :

You'll own our propensity modelling and wider machine learning initiatives end to end - framing the problem, building the features, training and validating the model, getting it into production, and keeping it healthy once real decisions depend on it.

This isn't a research seat. The value of a model here is what it changes in the product and the business, so you'll care as much about the serving path, monitoring, and retraining loop as you do about offline model lift.

You'll make architectural calls on features, training, deployment, and monitoring - and ship them. You'll also work directly with product, data, and business stakeholders, communicating clearly, disagreeing well, explaining models to non-technical audiences, and understanding the domain deeply.

What We Expect You to Be Strong At :

Required :

- ML in production : You have taken models all the way into production and lived with them afterwards - serving, monitoring, retraining, and the pager.

- Propensity & customer modelling : Hands-on with propensity, churn, conversion, or similar customer-outcome models.

- Feature engineering & data discipline : Feature stores, point-in-time correctness, and a healthy paranoia about target leakage and train/serve skew.

- Validation you can defend : Honest experimental design - proper splits, backtesting on time-ordered data, baselines, and knowing when a result is too good to be true.

- MLOps & lifecycle : Reproducible training pipelines, experiment tracking, model registry and versioning, CI/CD for models, and automated retraining.

- Databricks : Hands-on experience with Databricks, Spark, MLflow, Unity Catalog, Feature Store, and Model Serving.

- Compliance, governance & explainability : Model documentation and lineage, reproducibility, audit trails, approval workflows, and bias testing.

- Security in a multi-tenant SaaS : Tenant isolation is a first-class concern - no data crossing customer boundaries through features, training sets, caches, or logs.

- Strong engineering fundamentals : Production-quality Python and confident SQL. You write maintainable, tested code and treat pipelines as software rather than scripts.

- Systems thinking : You see how the pieces connect and where they'll break under load, over time, or at scale.

- Production-grade, at pace : You enjoy building things that hold up in production and think in hours and days, not weeks and months.

Non-Negotiable : LLM Usage

You use LLMs to multiply your own output. We want someone already deep in using LLMs to unblock themselves and move faster - driving an ecosystem with LLMs, wiring them into workflow, tooling, and the way the team ships.

- 1. Building at LLM speed - using models to prototype, refactor, and explore designs.

- 2. Coding agents and dev tooling - real fluency with the current generation of AI development tools.

- 3. Staying current - tracking what has changed, trying things, and bringing the team along.

Talk Us Through Your Work :

Demo is a bonus. We care far more about depth than polish. We want to sit with you and go deep on something real you've worked on.

- 1. What you built and why - the problem, options considered, and why you chose your approach.

- 2. What went wrong - where it failed, what surprised you, and what you'd do differently now.

- 3. The numbers behind it - how you measured performance, what it did in production versus offline.

- 4. The trade-offs you made - where you deliberately chose 'good enough,' and where you refused to compromise.

Nice to Have :

- Fintech or financial-services experience.

- Marketing, CRM, or campaign analytics.

- Streaming or near-real-time inference.

- Vibe coding experience.

- Startup or small product company background.