Job description
Join our fast-growing AI software company crafting next-generation intelligent systems. We are seeking a highly skilled AI Engineer to design and deploy production-grade models that power real-world applications. This role blends research, software engineering, and collaboration with cross-functional teams to deliver measurable business impact.
Based in San Francisco, this role offers remote-friendly options and a collaborative, inclusive culture with a strong emphasis on learning and innovation.
Responsibility
- Design, implement, and optimize end-to-end machine learning models for production systems with a focus on scalability and reliability.
- Collaborate with data scientists and software engineers to translate research into robust, maintainable code.
- Prototype and evaluate models across NLP, CV, and generative AI applications; monitor performance and drift.
- Develop ML pipelines and MLOps practices, including data versioning, feature stores, and CI/CD for models.
- Own model deployment, monitoring, and incident response in cloud environments (AWS/GCP).
- Implement rigorous testing, documentation, and code reviews to ensure quality and reproducibility.
- Mentor junior engineers and share knowledge through technical sessions and pair programming.
- Contribute to product strategy by translating customer needs into AI-powered solutions.
Qualification
- MS or PhD in Computer Science, Electrical Engineering, or a related field; or equivalent industry experience with strong portfolio.
- 3+ years of hands-on software engineering and ML model development in production.
- Proficient in Python and ML frameworks (PyTorch or TensorFlow) and experience with data processing (pandas, Spark).
- Experience with cloud ML platforms, containerization (Docker), and orchestration (Kubernetes).
- Strong understanding of ML fundamentals, model evaluation, bias, fairness, and deployment considerations.
- Excellent communication skills and ability to collaborate with cross-functional teams across product, design, and data science.
- Extremely organized with a bias toward action, strong problem-solving, and the ability to prioritize competing demands.
- U.S. work authorization or eligibility to work in the United States.