Senior ML Engineer (Token Factory)

Accountabilities:

• Drive inference optimization efforts by identifying bottlenecks and implementing performance improvements across diverse LLM architectures, improving throughput and reducing latency and cost per token.


• Contribute to the design and evolution of inference engines, including techniques such as speculative decoding, KV-cache optimization, and support for dense and MoE models.


• Develop and productionize low-precision training and inference pipelines (e.g., FP8, MXFP4) to maximize efficiency on large GPU clusters.


• Profile and analyze GPU workloads using modern tooling to identify performance constraints and guide architectural improvements.


• Collaborate on scalable distributed training and inference systems, including sharding strategies, custom kernels, and hardware-aware optimizations.


• Contribute to engineering best practices including testing, CI/CD, and maintainable production-grade ML systems.

Requirements

• Strong understanding of machine learning fundamentals, particularly transformer architectures and large language models.


• Hands-on experience profiling and optimizing GPU workloads using tools such as Nsight or PyTorch Profiler.


• Deep knowledge of GPU architecture, including memory hierarchy and compute vs. memory trade-offs.


• Familiarity with key LLM concepts such as attention mechanisms, RoPE, KV-cache, Flash Attention, and quantization techniques.


• Experience with large-scale deep learning training, including distributed systems, sharding strategies, and custom kernel development.


• Strong software engineering skills, with advanced proficiency in Python and modern ML frameworks.


• Solid understanding of software engineering practices such as version control, CI/CD pipelines, and unit testing.


• Strong communication skills with the ability to collaborate effectively in highly technical, cross-functional teams.

• Competitive compensation package


• Strong career development and continuous learning opportunities


• Flexible work environment with high autonomy and ownership


• Collaborative, innovation-driven engineering culture


• Opportunity to work on frontier AI systems at massive scale


• International, highly skilled, and diverse team environment

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