Machine Learning Scientist (Engineer)

What you'll get to do...

• Design and implement advanced machine learning algorithms for Pricing and Generative AI projects using PyTorch and TensorFlow 
• Develop and deploy machine learning models using Python and other state-of-the-art ML frameworks
• Build APIs to deliver real-time and batch-mode predictions to customers, leveraging AWS, Docker, and cloud technologies
• Monitor and optimize the performance, accuracy, and reliability of our ML solutions with logging, metrics, and dashboards
• Research and evaluate new ML techniques to enhance existing solutions and discover new opportunities
• Fine-tune models to improve performance, scalability, and adaptability
• Analyze complex datasets to inform model development and ensure accuracy
• Stay at the forefront of advancements in machine learning and AI, incorporating new methodologies as needed
• Collaborate with cross-functional teams to integrate ML solutions into broader product and platform initiatives
• Contribute to the development of standardized processes for model evaluation, validation, and production deployment
• Lead the exploration and adoption of innovative ML technologies, maintaining our competitive edge 

Your experience should include...

• Mid-Level: 4+ years of experience, including 2+ years in machine learning or related fields 
• Senior-Level: 7+ years of industry experience in software engineering and 4+ years in machine learning or related fields
• Proficiency in Python and machine learning algorithms, with experience in supervised/unsupervised learning, deep learning, NLP, and computer vision
• Experience deploying and monitoring ML models in production using AWS services such as SageMaker
• Hands-on experience building APIs for ML model serving with frameworks like Flask, FastAPI, or Django
• Strong understanding of software engineering best practices (version control, CI/CD, code reviews)
• Ability to work both independently and collaboratively in cross-functional teams
• Experience writing unit tests and documentation for ML code 

You might also have...

• Experience working with large-scale, complex datasets 
• Applied machine learning experience in industries like e-commerce, finance, or healthcare
• Familiarity with ML lifecycle management tools such as MLflow, Kubeflow, or Airflow
• Expertise with common ML libraries like TensorFlow, PyTorch, Keras, and Scikit-learn
• Hands-on experience with large language models (LLMs) and prompt engineering
• Proficiency in multiple programming languages
• Familiarity with containerization technologies like Docker and orchestration tools like Airflow