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EmaSan Francisco Bay Area, California, USA
Software Engineering Lead (Machine Learning)
On-siteFull Time$135k - $300k per yearPosted 3 days ago
About the role
- 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
- Lead the processing and analysis of 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
- We’re looking for an innovative and passionate Machine Learning Engineers to join our team
- You are someone who loves solving complex problems, enjoys the challenges of working with huge data sets, and has a knack for turning theoretical concepts into practical, scalable solutions
- You are a strong team player but also thrive in autonomous environments where your ideas can make a significant impact
- You love utilizing machine learning techniques to push the boundaries of what is possible within the realm of Natural Language Processing, Information Retrieval and related Machine Learning technologies
- Most importantly, you are excited to be part of a mission-oriented high-growth startup that can create a lasting impact
- Proficiency in Python and experience with ML libraries such as TensorFlow or PyTorch
- Good understanding of software development principles, data structures, and algorithms
- Proven industry experience in building and deploying production-level machine learning models
- The ability to work collaboratively in an extremely fast-paced, startup environment
- A Master’s degree or Ph.D. in Computer Science, Machine Learning, or a related quantitative field
- Familiarity with cloud platforms like GCP or Azure
- Excellent problem-solving skills, attention to detail, and a strong capacity for logical thinking
- Deep understanding of any of retrieval, ranking, reinforcement learning, and agent-based systems and experience in how to build them for large systems
- Excellent skills in data processing (SQL, ETL, data warehousing) and experience working with large-scale data systems
- Familiarity with the latest industry and academic trends in machine learning and AI, and the ability to apply this knowledge to practical projects
- Experience with machine learning model lifecycle management tools, and an understanding of MLOps principles and best practices
- Deep understanding and practical experience with NLP techniques and frameworks, including training and inference of large language models