Software Engineer, Machine Learning
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.
About the job
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.
Skills
Python, PyTorch, TensorFlow, NLP, Transformers, Reinforcement Learning, Retrieval, Ranking, MLOps, SQL
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