About Mutinex

We're an early-stage B2B SaaS startup with a proven platform, big-name clients, and millions in revenue. We're not chasing unicorn status; we're building a sustainable, long-lasting business (think Bowhead Whale!). Mutinex is on a mission to build a universal business growth decision engine! It empowers marketing, media, agency, analytics, and finance teams to organise, analyse, and action data at scale, unlocking and optimising the true drivers of sustainable business growth.

We're a hybrid team based in Sydney, Melbourne, and New York. We value communication, open-mindedness, and a culture of feedback.

Technical Challenge

High-dimensional causal inference. Many growth levers (marketing spend, pricing, trade promotions, owned media) and external factors (weather, inflation, seasonality) drive the same business outcome, at different levels of granularity. Our models separate the impact and contribution of each one.

We use a standard modelling approach across industries. We then tailor each model to the customer's context and validate it through rigorous quality checks. The result is a simple, reliable experience and clear recommendations that customers can act on.

Your job is to make this run reliably, fast and at scale for every customer.

The Role

Location: Sydney (hybrid, three days a week in the office)

We're hiring a Senior Machine Learning Engineer to take our models from research into production and turn them into product features that help customers plan their marketing investment. You'll work closely with data scientists, product managers and software engineers.

We're looking for someone who wants end-to-end impact, from understanding customer problems through to building the technical solution.

Our teams are cross-functional, bringing together data scientists, product managers and engineers. Data scientists design the models; you'll make them reliable, fast and available to every customer at scale.

What you'll do

• Shape the customer experience: Understand customer problems and lead the search for the best way to solve them. Partner with product managers and designers to turn those solutions into features customers praise.

• Bring models to production: Turn data science models into reliable production services. Integrate forecasting models into the planning workflows that customers use to make decisions.

• Scale optimisation: Build and scale the optimisation systems that turn forecasts into recommendations under real business constraints.

• Build backend services: Design and maintain the APIs and data layers that serve model results to our product (FastAPI, SQLAlchemy). Write clean, tested code and ship it through CI/CD.

• Automate the model lifecycle: Build event-driven pipelines (Cloud Run, Pub/Sub, Clickhouse) that onboard customers, refresh models with new data and release results, for many customers in parallel. Use infrastructure as code to keep them repeatable, observable and reliable.

• Protect model quality: Build automated checks that validate model results before they reach customers, and catch regressions early.

• Apply AI to our workflows: Build LLM-based agents and tools that automate model analysis and help our teams move faster.

What We're Looking For

• Machine learning: You understand the ML lifecycle, from training and evaluation to deployment and monitoring. You write strong Python and have hands-on experience with a framework such as PyTorch or TensorFlow. Experience with time-series or Bayesian modelling (for example JAX or NumPyro) is a plus.

• Software engineering: You've built production systems and taken proofs of concept to production. You know APIs, testing, CI/CD and deployment automation. You're comfortable with cloud infrastructure (we use GCP), infrastructure as code (Pulumi or Terraform) and containers (Docker, Kubernetes).

• Product mindset: You think beyond the code. You understand the business goal and the user need, and you shape your work around them. You're comfortable saying "I don't know" and closing the gap on the job.

• Root-cause thinking: You don't just fix bugs. You find the root cause, fix the system so the issue doesn't come back, and document what you learnt. You write code that is clean, well tested and easy for others to work with, not just code that works.

• Ownership and mentoring: You own customer problems from design to production. You write design documents, review technical proposals, and mentor engineers and data scientists.

You don't need to tick every box. If you match most of this and want to grow into the rest, we'd like to hear from you.

Why work with us?

• Impact at scale: Solve hard ML engineering problems at scale, and see your work shape how customers invest their marketing budget.

• Growth and team: Shape ML engineering practices across the company, with experienced data scientists and engineers who care about impact.

• Competitive salary and equity: A competitive salary, plus equity for every employee through our ESOP.

• Flexible work: Hybrid, with three days a week in our Sydney office. Up to 6 weeks a year working from anywhere, with at least 6 hours of overlap with Sydney working hours.

• Leave: 20 days annual leave, rising to 30 days with tenure. 12 weeks paid parental leave for primary caregivers and 6 weeks for secondary caregivers, after 1 year with Mutinex.

Ready to join us?

If you want to build ML systems that customers rely on to grow their business, we'd love to hear from you. Send us your resume and a short cover letter. In it, tell us about an ML system you took from prototype to production: what you built, what went wrong, and what you would do differently.