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Machine Learning Engineer - Inference
Machine Learning Engineer - Inference
Together AI is looking for a Machine Learning Engineer to join their Inference Engine team. This role involves optimizing and enhancing AI inference systems for large language models, ensuring efficient and effective operation at scale. The engineer will design and build production systems, develop runtime inference services, and collaborate on bringing new AI features to life.
About the role
About the Role
Together AI is seeking a Machine Learning Engineer to join our Inference Engine team, focusing on optimizing and enhancing the performance of our AI inference systems. This role involves working with state-of-the-art large language models models and ensuring they run efficiently and effectively at scale. If you are passionate about AI inference, PyTorch, and developing high-performance systems, we want to hear from you. This position offers the chance to collaborate closely with AI researchers and engineers to create cutting-edge AI solutions. Join us in shaping the future at Together AI!
Responsibilities
- Design and build the production systems that power the Together AI inference engine, enabling reliability and performance at scale.
- Develop and optimize runtime inference services for large-scale AI applications.
- Collaborate with researchers, engineers, product managers, and designers to bring new features and research capabilities to the world.
- Conduct design and code reviews to ensure high standards of quality.
- Create services, tools, and developer documentation to support the inference engine.
- Implement robust and fault-tolerant systems for data ingestion and processing.
Requirements
- 3+ years of experience writing high-performance, well-tested, production-quality code.
- Proficiency with Python and PyTorch.
- Demonstrated experience in building high performance libraries and tooling.
- Excellent understanding of low-level operating systems concepts including multi-threading, memory management, networking, storage, performance, and scale.
- Preferred: Knowledge of existing AI inference systems such as TGI, vLLM, TensorRT-LLM, Optimum.
- Preferred: Knowledge of AI inference techniques such as speculative decoding.
- Preferred: Knowledge of CUDA/Triton programming.
- Nice to have: Knowledge of Rust, Cython and compilers.