We are looking for a Deep Learning Engineer to help design, implement, optimize, and scale the training and inference systems behind our models.
What you'll do
Collaborate with cross-functional teams to bring research breakthroughs into production environments.
Build tools, abstractions, and infrastructure that make model training, evaluation, and deployment faster and more reliable.
Analyze and overcome performance and scalability challenges in large-scale distributed training.
Debug complex issues across the full ML stack: data loading, model code, distributed execution, GPU utilization, memory usage, networking, checkpointing, and serving.
Work with modern training and inference frameworks such as Megatron, TorchTitan, PyTorch, and vLLM.
Requirements: MSc or PhD in AI, Machine Learning or Algorithms.
Strong software engineering skills, with experience designing and building complex systems.
Practical understanding of distributed training concepts: data parallelism, tensor parallelism, pipeline parallelism, sharding, checkpointing, synchronization, and fault tolerance.
Experience working with inference systems and serving frameworks, ideally including vLLM or similar high-throughput LLM serving infrastructure.
Solid understanding of machine learning fundamentals, including optimization, transformers, attention mechanisms, numerical stability, and model evaluation.
Low-level systems intuition: performance profiling, memory layout, GPU utilization, networking bottlenecks, and tradeoffs between latency, throughput, and reliability.
Ability to write clean, maintainable, well-tested code in fast-moving environments.
Strong ownership mindset, good judgment, and the ability to operate with high agency under uncertainty.
This position is open to all candidates.