Senior Machine Learning Engineer

Company Description Morph is focused on specialized inference optimization for code generation, building systems that make large models for code faster, more efficient, and more reliable in production. The team works at the intersection of machine learning, systems engineering, and developer tooling to unlock high-performance AI-assisted coding. Morph emphasizes practical impact, designing solutions that can be integrated into real-world engineering workflows at scale. Team members collaborate in a fast-moving environment where experimentation, rigorous evaluation, and measurable improvements to model performance are central to the culture.Role Description As a Senior Machine Learning Engineer at Morph, you will design, implement, and optimize machine learning models and inference pipelines focused on code generation. You will develop and refine algorithms for efficient inference, including model compression, quantization, and serving optimizations tailored to real-world latency and throughput requirements. Day to day, you will analyze performance bottlenecks, run experiments, and build prototypes that improve reliability and scalability across our inference stack. You will collaborate closely with engineers and researchers to turn research concepts into production-ready systems, contribute to code reviews and technical design documents, and help shape best practices for ML engineering. This is a full-time, remote role, working with a distributed team that values clear communication, ownership, and outcome-driven development.Qualifications Strong foundation in core Computer Science and Algorithms, with experience designing and optimizing performant, maintainable code.Deep understanding of Neural Networks and Pattern Recognition, especially as applied to large-scale sequence models or code generation.Proficiency in Statistics and applied mathematical modeling for experimentation, evaluation, and performance analysis.Proven experience building end-to-end ML systems, including training, evaluation, and deployment of models in production environments.Advanced programming skills in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, JAX) and tooling for experiment management.Background in systems or infrastructure for ML (e.g., distributed training, GPU acceleration, model serving, or inference optimization) is highly beneficial.Bachelor’s or Master’s degree in Computer Science, Engineering, or a related quantitative field; a PhD or equivalent experience in ML or AI is a plus.Ability to work effectively in a remote, collaborative environment, communicate complex technical ideas clearly, and take ownership of end-to-end projects.