ML Engineer

Regions • Remote • 1m ago

The Role

We are seeking senior and staff-level Machine Learning Engineers to join a cutting-edge AI R&D team focused on applied research and the development of AI-intensive products. Recent research efforts have included:

Exploring test-time guided search to build more capable agents
Scaling task data collection to support reinforcement learning for software engineering agents
Enhancing the efficiency of LLM training on agentic trajectories

One of the key products developed by the team is an advanced inference and fine-tuning platform for AI models.

This position centers on optimizing training and inference performance in large-scale, multi-GPU, multi-node environments. It requires deep expertise in distributed systems and high-performance computing to build, optimize, and maintain robust pipelines.

Responsibilities

Architect and implement distributed training and inference pipelines using data, tensor, context, expert (MoE), and pipeline parallelism
Apply inference optimization techniques such as speculative decoding (and its extensions like Medusa, EAGLE), CUDA graphs, and compile-based optimization
Develop custom CUDA/Triton kernels for performance-critical neural network layers

Requirements

Strong theoretical foundation in machine learning
Deep understanding of performance aspects in large neural network training and inference (e.g., parallelism techniques, offloading, custom kernels, hardware utilization, attention optimizations, dynamic batching)
Expertise in at least one of the following areas:
- Custom efficient GPU kernel development (CUDA/Triton)
- Training large models across multiple nodes with parallelism techniques
- Inference optimization (e.g., disaggregated prefill/decode, paged attention, speculative decoding)
Strong software engineering skills (primarily Python)
Experience with modern deep learning frameworks (JAX and PyTorch)
Familiarity with CI/CD, version control, and unit testing
Excellent communication skills and ability to work independently

Nice to Have

Experience with modern LLM inference frameworks (e.g., vLLM, SGLang, TensorRT-LLM, Dynamo)
Familiarity with key concepts in the LLM space (e.g., MHA, RoPE, ZeRO/FSDP, Flash Attention, quantization)
Bachelor’s degree in Computer Science, AI, Data Science, or related field (Master’s or PhD preferred)
Proven track record of delivering products in dynamic, startup-like environments
Experience engineering complex systems (e.g., distributed data processing or high-load web services)
Contributions to open-source projects showcasing engineering expertise
Strong command of English, with excellent writing and communication skills

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