Senior machine learning engineer

Rockfish Data, Inc. has a hybrid Senior Machine Learning Engineer position in Pittsburgh, PA (with the ability to telecommute with appropriate telecommuting systems; the remote worksite must be within commuting distance of Pittsburgh, PA for weekly trips to the corporate office) responsible for designing, developing, and deploying production-scale generative AI models to create high-quality synthetic data for enterprise customers across industries including observability, cybersecurity, and Internet of Things (IoT). Specific duties include: (i) serving as a technical lead for the Machine Learning engineering team, as well as providing technical direction and mentorship to ML engineers across product development and customer engagement initiatives; (ii) guiding architectural decisions, establishing best practices for model development and deployment, and leading code reviews and technical planning sessions; and (iii) designing and implementing end-to-end pipelines for data ingestion, model training, evaluation, and synthetic data generation, as well as building LLM/NLP services using frameworks such as Hugging Face, OpenAI APIs, and custom transformer implementations.

Must have a master's degree (or foreign equivalent) in Computer Science, Electrical Engineering, Mathematics, Statistics, or related field plus five (5) years of experience in a related position.

Experience must include: (i) designing, developing, and deploying production-scale machine learning models using Python and modern deep learning frameworks (PyTorch or TensorFlow); (ii) building and deploying deep learning models, including neural architectures for representation learning, traditional machine learning models, and applying these to real-world applications; (iii) designing and implementing end-to-end pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment in production environments; (iv) deploying and managing ML workloads on cloud platforms (AWS, Google Cloud Platform, or Azure) including containerization technologies and scalable inference systems; (v) statistical learning, optimization algorithms, probability theory, and machine learning techniques for solving complex real-world problems; and (vi) MLOps practices including model lifecycle management, monitoring, CI/CD pipelines for model deployment, and maintaining production ML systems.

Experience can be concurrent.

This position is expected to pay an annual salary between $170,000 - $190,000.

To apply, email resume to [email protected].