Requirements
• Knowledge of modern deep learning techniques along with the ability to adapt these techniques to specific problems in the real world is the core requirement for this role,
• Experience with LLM/VLLM, transformers and generative AI for image and language based tasks is a plus,
• Direct experience with multi-modal models, combining language and images, is also a big plus,
• 5+ years industry experience developing machine learning models for image processing,
• Advanced degree in related field or equivalent industry experience,
• Expertise in applying machine learning based approaches using LLMs and multi-modal models,
• High level of familiarity with modern machine learning libraries including PyTorch or TensorFlow,
• Expertise with implementing numerical algorithms in Python,
• Proficiency with software version control systems,
• Familiarity with cloud computing platforms including AWS,
• Familiarity with issue tracking systems including Jira,
• Excellent written and verbal communication skills,
• Ability to be flexible, work analytically, and manage competing priorities in a high-growth environment,
• Knowledge and mindset for automation and improvements to streamline processes for scalability,
• A proactive approach to problem identification and independently developing/proposing remediation solutions,
• Ability to work independently with limited required direction, oversight and guidance on simultaneous projects and teams,
• Dynamic learning and growth mindset
What the job involves
• We’re looking for a Sr ML Engineer with a focus in Machine Learning to help expand the capabilities of the OpenSpace platform,
• You will be working with the rest of the AI team to apply deep learning techniques to our massive amount of visual data in order to provide meaningful insights to customers about their job sites,
• Design and architect complex, multi-component machine learning based systems,
• Prototype such systems and run detailed tests to evaluate performance on real world data,
• Work with platform engineers to develop production level implementations,
• Oversee dataset creation, curation, and labeling,
• Monitor system performance in production and ensure adequate performance,
• Assist in debugging and resolving failure edge cases in our production processing pipeline,
• Contribute to defining development processes, best practices, and documentation
