Machine Learning Engineer (SF Bay Area)

Ema - Bodega Bay, CA

Full-time - Mid Level

Bodega Bay, CA

Professional, Scientific, and Technical Services

About the position

Ema is seeking a passionate and innovative Machine Learning Engineer to join their team in the SF Bay Area. This role involves developing and deploying machine learning models that enhance various AI systems, particularly in Natural Language Processing (NLP) and related technologies. The ideal candidate will thrive in a hybrid work environment, collaborating with a talented team while also working autonomously to drive impactful solutions.

Responsibilities

• Conceptualize, develop, and deploy machine learning models for NLP, retrieval, ranking, reasoning, dialog, and code-generation systems.
• Implement advanced machine learning algorithms, including Transformer-based models, reinforcement learning, and ensemble learning.
• Lead the processing and analysis of large, complex datasets to inform model development.
• Manage the complete lifecycle of ML model development, including problem definition, data exploration, feature engineering, model training, validation, and deployment.
• Implement A/B testing and statistical methods to validate model effectiveness.
• Develop automated testing and validation processes to ensure the integrity of ML solutions.
• Communicate technical workings and benefits of ML models to both technical and non-technical stakeholders.

Requirements

• Master's degree or Ph.D. in Computer Science, Machine Learning, or a related quantitative field.
• Proven industry experience in building and deploying production-level machine learning models.
• Deep understanding and practical experience with NLP techniques and frameworks, including large language models.
• Experience with retrieval, ranking, reinforcement learning, and agent-based systems for large systems.
• Proficiency in Python and experience with ML libraries such as TensorFlow or PyTorch.
• Excellent skills in data processing (SQL, ETL, data warehousing) and experience with large-scale data systems.
• Experience with machine learning model lifecycle management tools and understanding of MLOps principles.
• Familiarity with cloud platforms like GCP or Azure.
• Knowledge of the latest industry and academic trends in machine learning and AI.
• Good understanding of software development principles, data structures, and algorithms.
• Excellent problem-solving skills and attention to detail.

Nice-to-haves

• Experience in a fast-paced startup environment.
• Familiarity with statistical methods for model validation.

Benefits

• Hybrid work environment with office presence three days a week.
• Opportunity to work with a talented team from leading tech companies.
• Engagement in innovative AI projects that have a significant impact.