About the role

Convert data science models into production-grade libraries.
Design and maintain large-scale ML pipelines.
Deploy models into production regularly.
Ensure reliability and scalability of models.
Collaborate closely with data scientists.

Skills the posting asks for

Python
MLOps
ML pipelines

Summarised from the employer’s posting, which is reproduced in full below.

The employer’s full posting

About PredictHQ

AI and forecasting systems have a blind spot: the real-world conditions that shift demand land as surprises instead of signals. PredictHQ closes that gap.

PredictHQ is the real-world context platform powering enterprise AI decisions, trusted by the world's largest enterprises, including Uber, Domino's and Accor. We explain more than 60 per cent of real-world demand variability, grounding models in verified spatial, temporal and economic reality so businesses can make high-stakes decisions on pricing, staffing and inventory with confidence.

With Beam, our relevancy engine, Bolt, our rapid integration framework, and native MCP support for AI agent workflows, we're defining a new category at the intersection of AI, data and enterprise decision-making. We're a company in motion, backed by strong fundamentals and hard technical problems.

Founded in 2016 and backed by Lightspeed Venture Partners, Sutter Hill Ventures, and Aspect Ventures, PredictHQ has offices in Auckland and San Francisco.

Our Values

PredictHQ is about teamwork to achieve huge things. We are building innovative and complex products, so our empathetic, hard-working culture is key to our success. We are proactive in helping our teammates grow and thrive in their careers, as well as making sure everyone can put their family and friends first.

Job purpose 🎯

We're looking for a Senior Machine Learning Engineer to join our Project team: a senior group of data scientists and engineers responsible for building and running the machine learning models that power how our customers understand and act on demand.

You'll be working with a unique combination of real-world context tracked globally, paired with real demand data - bookings, footfall, spend - from businesses across retail, hospitality, accommodation and transportation, at a scale nobody else has matched. That combination is what lets you build the models and systems behind our API-first products, giving global brands the ability to see not just what happens next, but why demand moves - so they can build that intelligence directly into their own forecasting, pricing and inventory systems. You'd be building the intelligence that powers enterprise AI decisions.

In this role, you'll own turning proven data science models into production-grade DS libraries and deploying them into our production environment - ensuring they run reliably at scale. You'll thrive here if you enjoy solving the challenging problems of production ML, including reliability, scalability, and integrating research-grade work into systems that withstand real-world loads.

You'll work hands-on with Python and our ML platform and infrastructure, using AI tools actively as part of how you build and ship. You'll work closely with our data scientists day to day, and contribute to the production libraries the team relies on.

What you'll do 🔑

Convert data science models and proofs-of-concept into production-grade ML libraries
Design, build and maintain large-scale ML pipelines from research through to serving
Validate that production libraries return the same results as the offline research models
Deploy models into production roughly every project cycle, working closely with the wider engineering team
Own the reliability and scalability of models once they're live
Work alongside data scientists during the research phase - feature engineering and offline testing
Contribute to and help evolve our MLE frameworks and engineering best practices
Mentor data scientists on building models that deploy more easily, and learn from them in return
Build models yourself where it makes sense (a smaller part of the role)

What you'll bring ⭐️

Expert-level Python, with hands-on experience deploying and maintaining production ML systems - not just building or prototyping them
Experience with distributed ML infrastructure - model serving (e.g., Ray Serve, SageMaker) and production ML pipelines
Experience with MLOps practices - CI/CD, automated pipeline testing, and model versioning/experiment tracking (e.g., MLflow)
4-5 years of engineering experience across software engineering, data engineering, and/or ML engineering
Strong software engineering methodology and best practices
Comfortable in a highly collaborative environment (standups, two-way code review, mentoring), and an active user of AI tools in your own work
Curiosity about frontier model architectures - we're building some of the most advanced models in the space; fast-moving tech or startup background preferred

General

Applicants for this position must have New Zealand residency or a valid New Zealand work visa.
Ensure that all activities are conducted in accordance with internal policies and procedures, applicable legislation, rules and standards, including relevant Acts, Advertising Standards Authority rules and regulations, and industry body requirements.
Based in Auckland, this role follows our hybrid approach, combining the flexibility of working from home with at least two days a week in the office to foster team connection and collaboration.

Benefits 💛

Health Insurance administered by Unimed
Paid Birthday Leave and Paid Family and Friends Day Leave
Strong focus on your training and development
10 weeks of fully paid parental leave
Flexible work arrangements
A hybrid work environment centred on collaboration, agility and fun
Options in a fast-growing company in its early stages
$500 annual stipend to support your work-from-home setup