Senior Machine Learning Engineer

JobSpace; Auckland; Hybrid; NZD 140,000 - 180,000; Full time

Benefits offered by this job

Health Insurance (Unimed)

Birthday Leave

Family & Friends Day Leave

Training & Development

Parental Leave (10 weeks)

Hybrid work environment

Work-from-home stipend

Job summary

PredictHQ is seeking a Senior Machine Learning Engineer to join our Project team, building and deploying production-grade ML libraries and large-scale pipelines that power demand forecasting and pricing decisions. You’ll work with Python and our ML platform, collaborating with data scientists to productionize research models.

You’ll own deployment, reliability, and scalability of models, mentor peers, and help evolve our ML engineering practices in a hybrid Auckland-based role requiring NZ

Qualifications

• Expert-level Python with production ML experience.
• Experience deploying and maintaining production ML systems.
• Experience with distributed ML infrastructure and model serving.
• Strong background in MLOps practices (CI/CD, testing, versioning).
• 4-5 years of engineering experience across software/data/ML engineering.

Responsibilities

• Convert data science models into production-grade ML libraries.
• Design and maintain large-scale ML pipelines from research to serving.
• Validate production results against offline models.
• Deploy models into production across project cycles with the wider team.
• Ensure reliability and scalability of live models.
• Collaborate with data scientists during research and feature engineering.
• Contribute to and evolve MLE frameworks and best practices.
• Mentor data scientists to build deployable models.

Skills

Python production ML

Distributed ML infra

MLOps CI/CD

Production ML pipelines

Model deployment

Collaborative work

Research-to-production

Tools

Ray Serve

SageMaker

MLflow

Job description

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.

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.
• 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
• 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