Machine Learning Engineer (Training Optimization)

About the Role

Join our core Generative AI team to invent foundational technologies that power the future of AI-assisted design. In this role, you will lead efforts to scale and optimize training systems for large-scale multimodal and foundation models, directly shaping the technical core of our creative intelligence engine.

Responsibilities

• Design, implement, and optimize large-scale machine learning systems for model training.
• Improve performance metrics, including GPU utilization, communication overhead, and memory efficiency.
• Partner with research and modeling teams to align system architecture with algorithmic requirements.
• Evaluate and apply industry best practices for distributed training.
• Profile and fine-tune training workflows to unlock new levels of scalability.

Requirements

• Strong background in LLMs, multimodal AI, or diffusion models.
• Proficiency in Python and familiarity with system programming languages like C++ or Rust.
• Deep knowledge of PyTorch or JAX and distributed training libraries such as Megatron-LM, NeMo, or DeepSpeed.
• Experience with optimization techniques including FSDP/ZeRO, gradient checkpointing, and low-precision data types.
• Hands-on experience writing custom GPU kernels in CUDA or Triton.
• Excellent communication and problem-solving skills with full proficiency in English.