A manager-level IC Machine Learning Engineer focused on end-to-end ML solution evaluation, implementation, and business optimization. This person will be responsible for evaluating and optimizing the off-the-shelf models from AWS, Azure, and Google Cloud, and quickly understanding business needs to propose effective machine learning solutions. The most critical aspects of this role are strong business problem-solving capabilities using machine learning solutions and fast engineering capabilities.

Qualifications

• Technical Proficiency: Strong programming skills in Python, R, or Java, and familiarity with ML frameworks such as TensorFlow, PyTorch, or scikit-learn. Proficiency with additional packages such as Keras, XGBoost, LightGBM, and H2O.ai.
• Data Handling: Expertise in handling large datasets, data preprocessing, and feature engineering using tools like pandas, NumPy, Dask, and Apache Spark.
• Machine Learning Evaluation: In-depth understanding of machine learning algorithms and evaluation metrics. Experience in assessing the performance of off-the-shelf models from AWS (e.g., SageMaker, Rekognition), Azure (e.g., Azure ML, Cognitive Services), and Google Cloud (e.g., AutoML, Vision AI).
• Strong Business Problem-Solving: Exceptional ability to quickly understand business processes and requirements, and propose effective machine learning solutions to optimize business outcomes. Strong problem-solving abilities and analytical thinking to evaluate and optimize ML models effectively.
• Fast Engineering Capability and implementations skills: Demonstrated ability to rapidly develop and implement machine learning solutions, adapting quickly to new technologies and business challenges. Proven experience in implementing ML models in production environments, ensuring seamless integration and performance optimization.
• Software Development: Strong experience with the full software development lifecycle, including coding standards, code reviews, source control management, build processes, testing, and operations.
  
  

Preferred Qualifications

• Advanced Degree: A Master’s or PhD in Computer Science, Data Science, or a related quantitative or engineering field.
• Industry Experience: Proven experience in evaluating, deploying, and implementing ML models in a production environment.
• Large Language Model Experience: Knowledge and experience with large language models such as OpenAI's GPT-3, LLama, and other similar models. Ability to evaluate and fine-tune these models for specific applications.
• Communication Skills: Excellent communication skills to articulate technical concepts to non-technical stakeholders and collaborate effectively within a team.
  
  

A manager-level IC Machine Learning Engineer focused on end-to-end ML solution evaluation, implementation, and business optimization. This person will be responsible for evaluating and optimizing the off-the-shelf models from AWS, Azure, and Google Cloud, and quickly understanding business needs to propose effective machine learning solutions. The most critical aspects of this role are strong business problem-solving capabilities using machine learning solutions and fast engineering capabilities.

• Problem solver: Quickly grasp business requirements and challenges. Propose and implement machine learning solutions that optimize business processes and outcomes.
• End-to-End Implementation: Deploy machine learning models into production environments, ensuring seamless integration with existing systems and optimal performance.
• Model Evaluation: Evaluate off-the-shelf machine learning models, including those from AWS (SageMaker, Rekognition), Azure (Azure Machine Learning, Cognitive Services), and Google Cloud (AutoML, Vision AI). Assess their suitability for specific business problems, and perform thorough model validation, including accuracy, performance, and robustness checks.
• Model Optimization: Continuously monitor and optimize models for performance and scalability. Implement necessary adjustments and retraining to maintain high standards.
• Collaboration: Work closely with cross-functional teams, including data scientists, software engineers, and product managers, to understand requirements and deliver robust ML solutions.
• System Design: Develop and maintain scalable ML pipelines and infrastructure that facilitate efficient model deployment and monitoring.
  
  

Innovation: Stay updated with the latest advancements in machine learning research and incorporate new techniques and tools to improve existing systems.