Senior Software Engineer: Machine Learning Infrastructure and Tools at Halter

Auckland, Auckland, New Zealand -

Full Time

Start Date

Immediate

Expiry Date

31 May, 26

Salary

0.0

Posted On

02 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning Infrastructure, MLOps, Python, AWS, GCP, Azure, Docker, Kubernetes, TensorFlow, PyTorch, Terraform, SQL, NoSQL, Kafka, Spark Streaming, CI/CD

Industry

Description

About Halter At Halter, we’re on a mission to enable farmers and graziers to run the most productive and sustainable operations. Our customers are using Halter to break free from the time-intensive constraints of conventional practices. Imagine watching 500 cattle stand up and walk calmly towards their next break? No quad bikes, no dogs, no fences. Just a group of cattle walking at their own pace. People say it looks like magic. Our customers are revolutionizing grazing with Halter. It's changing lives and transforming an industry. People join Halter to do meaningful work. By joining us you’ll be solving challenging problems within a talented team and a culture built for high performance. Our team out-think, out-work and out-care. We’re committed to delivering real change in the world - this isn’t easy, and in truth, we love that it’s hard. We’re backed to deliver on a mission that matters by Tier 1 investors including Bessemer Venture Partners, BOND, DCVC, Blackbird, Promus Ventures, Rocket Lab’s Peter Beck and Icehouse ventures. To find out more, visit our LinkedIn & Instagram. Machine learning infrastructure underpins all of our data products, and enables R&D on highly complex systems with the potential to unlock untapped value. We are looking for a Senior Machine Learning Infrastructure Engineer who can scale our market-leading behaviour models, enable the execution of scientific endeavors in a deep-learning dominant environment, and extend the way we apply machine learning to all areas of the business. We’re looking for people who are hungry to make an impact, are comfortable in a fast-paced environment, and love helping the people around them succeed. What your day could look like: Design and maintain scalable ML pipelines for training, validation, and inference Build and optimize model serving infrastructure with proper monitoring, logging, and alerting Implement MLOps practices including automated testing, deployment, and rollback systems Manage data pipelines and ensure data quality, lineage, and governance Optimize model performance and resource utilization across different environments Collaborate with data scientists to productionize models and research experiments Technical Skills: Strong proficiency in cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes)Experience with ML frameworks (TensorFlow, PyTorch) and serving systems Knowledge of orchestration tools Proficiency in Python, SQL, and Infrastructure as Code (Terraform) Experience with monitoring and observability tools Understanding of CI/CD pipelines and version control systems Infrastructure Skills: Database management (both SQL and NoSQL) and data warehousing solutions Stream processing and real-time data systems (Kafka, Spark Streaming) Model registry and experiment tracking systems Performance optimization and cost management in cloud environments What we’re looking for A comprehensive understanding of the fundamentals and design systems behind building reliable, scalable and fit-for-purpose machine learning models and infrastructure Strong experience in Python and familiarity with development in a backend tech stack A strong understanding of data engineering best practices and AWS infrastructure A confident understanding of data engineering best practices and AWS infrastructure Importantly, we are looking for someone who is passionate about what they do and eager to learn Join our team Halter is committed to promoting a diverse and inclusive workplace — a place where we can each be ourselves and do the best work of our lives. Research shows that while men apply to jobs when they meet an average of 60% of the requirements, women and under-represented groups of candidates tend to only apply when they meet every requirement. If you think you have what it takes but don’t necessarily tick every requirement on this job description, please still get in touch and apply to Halter. We’d love to chat to see if you’ll be an epic fit! If this opportunity sounds like you, please apply below by sending through your cover letter explaining why you’re excited about this role and working at Halter, along with your CV, and we’ll be in touch! Please also feel free to check out the careers page for more information on working at Halter and don't forget to follow us on LinkedIn & Instagram. Why our team loves working at Halter: Work that genuinely matters. Every now and again a company comes along that transforms an entire industry and leaves the world in a better place. Our team gets to be part of something truly meaningful, helping farmers improve their livelihoods, spend more time with their families, and build more sustainable operations. Spectacular people solving hard problems. Our culture is designed for talented people to do work that changes lives. The team is filled with diverse, kind, and driven people who push each other to do their best work. You'll be thrown into the deep end, tackling complex challenges and building something tangible that solves real problems. You'll grow here. Autonomy, mastery, and learning define how we work. You'll have the freedom to work on interesting problems, master new skills, and continuously develop yourself, both through your role and our $1,000 personal growth fund. This isn't easy, and we love that it's hard. Working at Halter will be the most rewarding and the most challenging work of your life. We move fast, take bold bets, and work hard to reshape an entire industry. As one team member put it: "Joining Halter is a bit like strapping yourself to a rocket ship, but it's an epic journey to be a part of!"

Responsibilities

The role involves designing and maintaining scalable Machine Learning pipelines for training, validation, and inference, alongside building and optimizing model serving infrastructure with monitoring and alerting. Responsibilities also include implementing MLOps practices, managing data pipelines, and collaborating with data scientists to productionize models and research experiments.