Lead development of Agentic AI capabilities and LLM applications for multiple mission management applications. Mentor teams to implement ML algorithms addressing customer challenges.
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
Staff AI/ML Engineer at CACI responsible for developing AI/ML algorithms and analyzing datasets. Join a high - performing team supporting national safety missions.
AI/ML Engineer at CACI developing machine learning algorithms for multiple applications. Collaborating with a research team to implement cutting - edge AI/ML solutions for customer missions.
Senior Computer Vision AI/ML Engineer leading a team in AI/ML algorithm implementation for remote sensing solutions. Responsibilities include training models and analyzing datasets with a focus on defense and commercial applications.
MLOps Engineer working on ML processes and robust workflows at Kensho. Collaborating with engineers to enhance tooling, services, and frameworks for machine learning.
Intern working on avionics hardware and software for Blue Origin. Gaining hands - on experience while contributing to space development projects.
Machine Learning Engineer engaged in recommendation and search, NLP, and image processing. Contributing to a major online dating app, Pairs, in Japan.
Senior Scientist II leading innovative AI and machine learning projects in oncology at Tempus. Collaborating with teams to advance predictive modeling and drug R&D initiatives.
Technical leader architecting and deploying advanced Gen AI and ML solutions for Walmart Connect. Driving innovation in ad targeting, personalization, and campaign analytics.
MLOps Engineer scaling AI/ML solutions across game studios at Stillfront. Collaborating with teams to operationalize machine learning solutions for a diverse gaming portfolio.