Master Thesis focusing on developing machine learning models for lithium - ion cell sorting at Fraunhofer LBF. Involvement in innovative projects addressing circular economy in battery recycling.
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
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