End-to-End Machine Learning and Deep Learning Model Development:
From initial data gathering, EDA, basic data cleaning, annotation to deploying models in production.
Domain Knowledge in one of NLP/Audio/CV:
Apply expertise in NLP, including in text classification, named entity recognition, and sequence labeling. Preferably to have experience of LLM, including quantitation, fine-tuning.
Apply expertise in Audio, including in Audio Classification, ASR. Preferably to have experience of Transformer based model training.
Apply expertise in Computer Vision, including object detection, segmentation, tracking. Preferably to have experience of Vision Language Model.
Technical Proficiency:
Demonstrate advanced skills in Python programming, PyTorch, sklearn, pandas, Docker, and REST API development.
Model Selection, Training, and Validation:
Develop and train machine learning or deep learning models, employing SOTA techniques and algorithms.
Conduct thorough model selection processes, comparing and evaluating various models to determine the best fit for specific tasks.
Testing, Benchmarking, and Scaling Models:
Rigorously test models under various scenarios to ensure reliability and robustness.
Benchmark model performance against industry standards and scale models to handle large-scale data efficiently.
Comply with QHSE (Quality Health Safety and Environment), Business Continuity, Information Security, Privacy, Risk, Compliance Management and Governance of Organizations policies, procedures, plans and related risk assessments.
Requirements:
Bachelor's Degree in Artificial Intelligence, Business Analytics, Data Science, Computer Science, Mathematics, Engineering or related field.
3 years of experience in end-to-end machine learning and deep learning model training on GPU servers with parallelism experience. Preferable to have experience working on AMD GPU.
Ideally, you'll also need:
A commitment to research.
A highly data-focused and investigative approach to problem-solving.