Applied Scientist 3

Key Responsibilities

1. Development of advanced AI models and algorithms, focusing on large language model, large multimodal, computer vision and foundation models.
2. Design, implement and test the critical module/features of AI service that are correct, highly available, scalable, and cost-effective.
3. Champion best practices for testing, benchmarking, and model validation to ensure reliability and performance.
4. Analysis of ML models, and optimizing models for accuracy and latency.
5. Large-scale training & production deployment with ML models.
6. Own data analysis, feature engineering, technique selection & implementation, debugging, and maintenance of production model.
7. Experience implementing machine learning algorithms or research papers from scratch to production.
8. Work with large, complex data sets.
9. Proactively identify the technical issues/bugs and provide innovative solutions.
10. File patent and publication as by product of solving complex business problems
11. Partner closely with product managers, engineering leads, and annotation/data teams to define requirements, data quality assurance and acceptance of data/annotation as required.
12. Leverage Oracle Cloud technology.

Preferred Qualifications

Ph.D. (preferred) or Master’s in Computer Science, Machine Learning, Computer Vision, or related field.

1. PhD in computer vision or 2+ years of Experience designing, implementing and deploying computer vision models in production environments

Expertise in GenAI, LLMs, LMMs, object detection, facial recognition, and image classification.

Strong foundation in deep learning architectures such as CNNs, transformers, diffusion models, and multimodal models.

1. Expert in at least one high level language such as Python/Java/C++
2. Practical experience in ML algorithm design, model training and production deployment using microservices architecture
3. Practical experience working in a cloud environment: Oracle Cloud (OCI), AWS, GCP, Azure or similar technology.
4. Experience or willingness to learn and work in Agile and iterative development processes.
5. Strong drive to learn and master new technologies and techniques.
6. Deep understanding of data structures, algorithms, and excellent problem-solving skills.
7. You enjoy a fast-paced work environment.

Responsibilities

Identify data science use cases and design scalable solutions that can be built as a feature of the product/service. Contributes to writing production model code. Work with Software Engineering teams to deploy them in production. Set up environment needed to run experiments for all projects. Set up distributed environments. Design and implement algorithms, train models, and deploy both to production to validate premises and achieve goals. Design and execute offline/online experiments and model performance testing. Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. Address business/customer problems and questions using statistical and machine learning techniques to achieve business goals and KPI's. Come up with innovative solutions to address tradeoffs or challenges faced by team. Stay up-to date with research and trends regarding latest algorithms in ML or other industry/domain space. Perform research in emerging areas, which may include efficient neural network development including quantization, pruning, compression and neural architecture search, as well as novel differentiable compute primitives. May perform other duties as assigned.