Software Engineer, Machine Learning

Ema•Mountain View, CA

2d•$160,000 - $220,000

About The Position

Ema is hiring a Machine Learning Engineer to develop and deploy machine learning systems supporting its AI Employees for HR, IT, and Finance. The role focuses on NLP, information retrieval, ranking, reasoning, dialogue, and code generation, and involves turning theoretical methods into practical, scalable production solutions. The position spans the full machine learning model lifecycle, from problem definition and data exploration through feature engineering, training, validation, and deployment. The engineer will work with large, complex datasets, validate model effectiveness through experiments and statistical methods, and communicate model capabilities to technical and non-technical stakeholders. Ema describes itself as a high-growth startup building agentic AI systems for real business processes at scale.

Requirements

Master’s degree or Ph.D. in Computer Science, Machine Learning, or a related quantitative field.
At least 2 years of industry experience 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 at least one of retrieval, ranking, reinforcement learning, or agent-based systems, and experience building such systems for large-scale applications.
Proficiency in Python.
Experience with machine learning libraries such as TensorFlow or PyTorch.

Nice To Haves

Strong data-processing skills, including SQL, ETL, and data warehousing, and experience with large-scale data systems.
Experience with machine learning model lifecycle management tools and understanding of MLOps principles and best practices.
Familiarity with cloud platforms such as GCP or Azure.
Familiarity with current industry and academic trends in machine learning and AI, and ability to apply this knowledge to practical projects.
Good understanding of software development principles, data structures, and algorithms.
Excellent problem-solving skills, attention to detail, and strong logical thinking.
Ability to collaborate in an extremely fast-paced startup environment.

Responsibilities

Conceptualize, develop, and deploy machine learning models for NLP, retrieval, ranking, reasoning, dialogue, and code-generation systems.
Implement and improve machine learning algorithms and systems, including Transformer-based models, reinforcement learning, ensemble learning, and agent-based systems.
Process and analyze large, complex structured, semi-structured, and unstructured datasets to inform model development.
Manage the complete machine learning model lifecycle, including problem definition, data exploration, feature engineering, model training, validation, and deployment.
Use A/B testing and other statistical methods to evaluate model effectiveness.
Develop automated testing and validation processes to ensure the integrity and robustness of machine learning solutions.
Explain the technical workings and benefits of machine learning models to technical and non-technical stakeholders.