Machine Learning Engineer designing and optimizing deep learning models for safety - critical environments at Destinus. Shaping the future of high - speed, autonomous flight technologies.
About the role
- Machine Learning Engineer developing machine learning models and integrating them into construction technology solutions. Collaborating with engineering teams at Timescapes to enhance product capabilities.
Responsibilities
- Working closely with other members of the AI / ML team to design, develop, integrate and support machine learning models, to solve specific construction problems related to tracking activity and progress on civil, institutional and commercial sites.
- Running experiments with LLMs and other large-scale generative AI models during problem and solution exploration, and determining when to invest in custom development vs leveraging off-the-shelf models.
- Developing software to manage the LM lifecycle, including data management, labeling and training.
- Sourcing training data, curating it for quality, and determining how best to integrate it into the ML lifecycle.
Requirements
- Bachelor’s degree in Computer Science, Engineering, or a related field.
- Experience with the development and delivery of production-level software systems that feature critical machine learning components.
- Experience with the development of computer vision models, from the data side as well as the model side.
- Experience with end-to-end ML operations, including data acquisition, labeling, model training, pipeline design + management, continuous integration, model versioning, and performance monitoring.
- Experience with leveraging LLMs, LVMs and multimodality models to augment product capabilities, including strong experimentation skills, and the ability to combine off-the-shelf commercial model capabilities with custom ML models.
- Familiarity with methods for ML model validation and verification.
- Experience deploying ML inference infrastructures for systems with high throughput.
- Ability to develop ML-enabled product capabilities in the context of the entire SDLC, from initial concepts through detailed design, development, deployment and maintenance.
- Experience with deep learning frameworks e.g. PyTorch
- Familiarity with inference engines e.g. TensorRT
- Experience with developing edge ML applications
- Experience writing with .NET, C# and Python
Benefits
- Competitive compensation and equity package
- Health insurance
- 5% kiwisaver contribution
- EAP & wellbeing support
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
Machine Learning Engineer optimizing personalization systems for Spotify's audio streaming service. Collaborating with cross - functional teams to enhance user experience and deliver recommendations.
Principal Machine Learning Engineer developing ML and GenAI solutions in a cloud - native environment at Flexera. Leading a high - impact team and driving operational excellence for ML infrastructure.
Senior ML Platform/Ops Engineer building ML systems for AI - powered learning at Preply. Productionizing machine learning with high reliability, performance, and observability in a hybrid environment.
Senior ML Platform/Ops Engineer building AI - powered ML pipelines for a dynamic Ed - Tech company. Collaborating with ML scientists and engineers to ensure reliable deployment and observability.
Senior ML Platform/Ops Engineer at Preply building and maintaining ML pipelines, collaborating with ML Scientists and Data Engineers.
Machine Learning Engineer developing advanced Deep Learning models for autonomous driving technology at Mobileye. Collaborating in a high - end algorithmic engineering team on critical computer vision challenges.
Machine Learning Engineer focusing on vulnerabilities and security of AI systems at Carnegie Mellon University. Collaborating with a team to build robust prototypes and provide solutions for government sponsors.
Lead machine learning engineer developing solutions for Army enterprise AI and ML team. Collaborating with experts to deliver cutting - edge analytics and models for real - world challenges.
Machine Learning & Signal Processing Scientist at BlueGreen Water Technologies analyzing multi - source environmental data. Focused on developing algorithms and models for signal processing and machine learning techniques.