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Halter is a venture-backed agtech company transforming grazing management through precision livestock technology. The company enables farmers and graziers to optimize operations sustainably using advanced predictive systems, remote sensing, and animal health monitoring—without traditional infrastructure like fences, quad bikes, or herding dogs.
The Predictive Systems group develops cutting-edge machine learning models to provide farmers with actionable insights. You will own end-to-end development of ML pipelines—from research and model architecture design through deployment and production monitoring. Your work will span diverse applications: pasture biomass estimation via remote sensing, animal heat event detection, health and activity modeling, and grazing interval simulation for farm recommendations.
Key responsibilities include:
- Designing and developing predictive systems to solve real-world farming challenges
- Conducting literature reviews across both ML and pasture science to stay at the frontier
- Owning architectural design, dataset construction, training, and optimization of novel models
- Supporting deployed systems in production and learning from field behavior
- Proactively innovating across the data stack to deliver high-impact solutions
- Collaborating with engineering, product, and design teams to achieve outsized results
You will work with autonomy across infrastructure, databases, simulation, and ML development. The role demands both technical depth in machine learning and genuine curiosity about agricultural domain knowledge. Halter operates an office-first culture in Auckland, with flexibility for genuine business needs. The company is backed by Tier 1 investors including Founders Fund, Bessemer Venture Partners, DCVC, and others.
REQUIREMENTS:
- Deep academic and/or professional background applying machine learning to real-world problems (e.g., postgraduate degree in mathematics or computer science with focus on deep neural networks and modeling)
- Demonstrated experience designing, deploying, and monitoring production ML systems
- Strong causal reasoning and ability to design models reflecting real-world dynamics
- Strong data intuition for working with noisy, high-volume, high-dimensional datasets
- Fluency in Python and experience contributing to complex, collaborative codebases
- Proficiency with contemporary AI tools (Copilot, ChatGPT, Cursor, Claude)
- Excellent communication and collaborative skills across disciplines
- Plus: experience with advanced ML architectures (Transformers, SSMs), agent-based systems, or recommendation systems
