Job Overview
Our client is looking for a Machine Learning Engineer to keep their pricing engine running reliably in production. Your focus is the machinery around the models: the pipelines that train and run them, the infrastructure they run on, and the monitoring that tells us when a model or a daily pricing run has gone wrong.
This is a hands-on contributor role in a small, distributed team. You will pick up well-defined pieces of work and see them through, with growing ownership of the operational side of the engine as you learn the domain. Deep modelling expertise is not expected on day one, though you will be working alongside the models every day. Monitoring and observability is an area our client is actively investing in, and it is where this role will have the most immediate impact.
Client Timezone: Syd, Australian Eastern Standard Time
Responsibilities
- Building and maintaining the containerised pipelines that train models and produce daily price recommendations, along with the AWS scheduling and queueing that drives them.
- Watching daily pricing runs across every customer: catching failed or degraded runs, working through dead-letter queues, and getting a run back on track before it affects a customer's prices.
- Building out model performance monitoring — tracking prediction accuracy over time, input data drift, and how recommendations compare to what was actually sold.
- Extending the automated checks that stop a bad training run reaching production.
- Maintaining the model registry: versioned artefacts, and the assignments that decide which model version serves which customer.
- Owning CI/CD for the ML repository — build, test, package and release across a monorepo of independently versioned packages.
- Writing and maintaining the Terraform / OpenTofu that defines the ML infrastructure.
- Supporting the team's modelling work: preparing data, reproducing results, and productionising experiments once they graduate.
- Improving the runtime and cost of training and inference jobs.
Tech Stack
- Backend: Python, FastAPI, Node.js, GraphQL, REST APIs · Rust an advantage
- Cloud & DevOps: Docker, AWS (EC2, S3, Lambda and similar), Git, CI/CD, infrastructure as code (Terraform / OpenTofu)
- Testing: pytest, Playwright and similar automated testing frameworks
- Data: Columnar dataframes and Parquet, PostgreSQL, SQL
- Orchestration: Scheduled and event-driven pipelines for model training and batch inference
- Model Operations: Model registry and versioned artefacts, reproducible training runs, automated validation before release
- Monitoring: Pipeline and job observability, model performance and data drift tracking, product analytics
- AI Tooling: Claude Code, Cursor and similar AI-assisted development tools
- Analysis: Notebooks and Python visualisation libraries
Requirements
- Commercial experience as a machine learning engineer, MLOps engineer, data engineer, or backend engineer working closely with ML systems in production.
- Strong Python · modern, type-annotated code, properly packaged and tested.
- Experience running things in production on AWS: containers, scheduled or event-driven jobs, queues, and object storage.
- Infrastructure as code (Terraform, OpenTofu or CDK) and CI/CD pipelines.
- Practical monitoring and observability experience — logs, metrics, alerting, and the judgement to know what is worth alerting on.
- Solid SQL and relational data skills.
- Automated testing as a normal part of delivery, not an afterthought.
- Working fluently with AI coding tools such as Claude Code or Cursor as part of your day-to-day delivery.
- Clear written communication, and comfort working in a distributed team where technical decisions are documented and debated in writing.
- Comfortable working to a direction set by someone else, and confident asking for it when it is not clear.
Preferred Qualifications
- Model performance monitoring and drift detection in production.
- Experience with an ML platform or model registry (MLflow, SageMaker, Metaflow, Kubeflow) — ours is in-house, so the concepts transfer more than the tool.
- Familiarity with gradient boosting and tabular machine learning; any exposure to causal inference or sequential decision making is a bonus.
- Product analytics tooling (PostHog, Amplitude, Mixpanel or similar), and using product usage data alongside model metrics to understand how recommendations are actually being used.
- Comfort reasoning about memory and throughput on large datasets.
- Multi-tenant SaaS architecture, including tenant isolation and per-customer model versioning.
- Dynamic pricing, revenue management, yield management or e-commerce pricing experience.
- Experience in the finance or fintech sector, particularly where automated decisions carry commercial consequences and need to be auditable.
- Rust, which we are progressively adopting for backend services.
Independent Contractor Perks
- Health Insurance Coverage for eligible locations
- Permanent work from home
- Immediate hiring
Note
Please click the "Apply" button to complete your application, including the assessment questions, technical check, and voice recording. Your hourly pay rate will be established based on your performance in the application process; submissions with all requirements fulfilled will receive priority review.
