Develops and deploys ML models for NLP, retrieval, ranking, reasoning, dialog, and code-generation systems. Requires Master's/PhD, 2+ years experience with production ML, deep NLP expertise, Python, and frameworks like PyTorch/TensorFlow.
135k – 200k/yr
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About the role
Responsibilities
Conceptualize, develop, and deploy machine learning models that underpin our NLP, retrieval, ranking, reasoning, dialog and code-generation systems.
Implement advanced machine learning algorithms, such as Transformer-based models, reinforcement learning, ensemble learning, and agent-based systems to continually improve the performance of our AI systems.
Process and analyze large, complex datasets (structured, semi-structured, and unstructured), and use your findings to inform the development of our models.
Work across the complete lifecycle of ML model development, including problem definition, data exploration, feature engineering, model training, validation, and deployment.
Implement A/B testing and other statistical methods to validate the effectiveness of models. Ensure the integrity and robustness of ML solutions by developing automated testing and validation processes.
Clearly communicate the technical workings and benefits of ML models to both technical and non-technical stakeholders, facilitating understanding and adoption.
Minimum Qualifications
A Master's degree or Ph.D. in Computer Science, Machine Learning, or a related quantitative field.
At least 2 years of industry experience in building and deploying production-level machine learning models.
Deep understanding and practical experience with NLP techniques and frameworks, including training and inference of large language models.
Deep understanding of any of retrieval, ranking, reinforcement learning, and agent-based systems and experience in how to build them for large systems.
Proficiency in Python and experience with ML libraries such as TensorFlow or PyTorch.
Ideally, You'd Have
Excellent skills in data processing (SQL, ETL, data warehousing) and experience working with large-scale data systems.
Experience with machine learning model lifecycle management tools, and an understanding of MLOps principles and best practices.
Familiarity with cloud platforms like GCP or Azure.
Familiarity with the latest industry and academic trends in machine learning and AI, and the ability to apply this knowledge to practical projects.
Good understanding of software development principles, data structures, and algorithms.
Compensation (California Based)
Standard base salary: $135,000 to $200,000 annually.
Compensation determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.
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