Senior Machine Learning Engineer

About PredictHQ

PredictHQ enhances AI and forecasting systems by providing crucial real-world context that uncovers the true signals behind demand changes, rather than surprises. Trusted by large global enterprises like Uber, Domino's, and Accor, our platform explains over 60% of real-world demand variability through verified spatial, temporal, and economic data. This empowers businesses to make informed decisions on pricing, staffing, and inventory management.

Our core technologies include Beam, a relevancy engine; Bolt, a rapid integration framework; and native MCP support empowering AI agent workflows. Since our founding in 2016, supported by prominent venture capital firms, we operate in Auckland and San Francisco, driven by complex technical challenges and strong foundations.

We foster a culture of teamwork, empathy, and dedication, supporting personal growth and balancing work with family priorities.

Job Purpose

We invite an experienced Senior Machine Learning Engineer to join our Project team, a group dedicated to developing and maintaining machine learning models that help clients comprehend and act on demand signals. The role focuses on leveraging unique, globally tracked real-world context paired with demand data across sectors like retail, hospitality, accommodation, and transportation. Your work will shape intelligence embedded into enterprise forecasting, pricing, and inventory systems, driving strategic AI decisions.

Key Responsibilities

• Transform data science models and prototypes into robust, production-quality ML libraries.
• Design, implement, and sustain large-scale machine learning pipelines from research stages through deployment.
• Ensure production model outputs align precisely with offline research results.
• Deploy and update models regularly within the production environment in collaboration with engineering teams.
• Maintain model reliability and scalability post-deployment.
• Collaborate with data scientists on feature engineering and offline model validation.
• Enhance MLE frameworks and establish sound engineering practices.
• Mentor data scientists on deployable model design and gain insights from their expertise.
• Occasionally develop machine learning models directly where appropriate.

Required Qualifications and Experience

• Expert proficiency in Python with proven experience in deploying and maintaining production machine learning systems.
• Familiarity with distributed ML infrastructures and model serving technologies like Ray Serve or SageMaker.
• Experience implementing MLOps methodologies including CI/CD pipelines, automated tests, and model versioning tools such as MLflow.
• 4-5 years of combined experience in software, data, or machine learning engineering roles.
• Strong foundations in software engineering principles and best practices.
• Ability to thrive in collaborative environments, actively participating in code reviews, standups, and mentoring.
• Curiosity and knowledge of advanced model architectures and fast-evolving technologies, preferably from startup or cutting-edge tech settings.
• Applicants must have New Zealand residency or a valid work visa.

Other Information

• Role is based in Auckland with a hybrid work model requiring at least two days per week onsite to encourage collaboration.
• Compliance with all relevant internal policies, legislation, advertising standards, and industry requirements is mandatory.

Benefits

• Health insurance coverage through Unimed.
• Paid leave for birthdays and family/friends days.
• Commitment to ongoing professional training and development.
• 10 weeks of fully paid parental leave.
• Flexible working arrangements and a supportive hybrid environment focused on teamwork, agility, and enjoyment.
• Opportunity to acquire equity options in a growing early-stage company.
• An annual $500 stipend to enhance your home office setup.