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Machine Learning Engineer, Model Bring-Up
Bring transformer models onto Cerebras AI accelerator hardware and optimize their inference performance. Develop MLIR compiler paths while improving model correctness, latency, throughput, and memory efficiency.
Descriere
- Bring new models into production by analyzing architectures, converting weights, implementing execution paths, and validating results against reference implementations.
- Lower models to Cerebras hardware with MLIR dialects, graph transformations, lowering passes, and hardware-specific mappings.
- Support attention, matrix multiplication, normalization, positional embeddings, and other operators through compiler and kernel development.
- Optimize operator fusion, tensor layouts, tiling, memory allocation, data movement, and parallel execution.
- Improve prefill and decode performance by tuning KV-cache management, batching, and quantization for better latency, throughput, and memory use.
- Analyze numerical discrepancies and assess how precision changes and compiler optimizations affect accuracy.
- Use profilers, execution traces, and hardware counters to locate compute, memory, communication, and runtime bottlenecks.
- Partner with hardware, compiler, kernel, and runtime teams to deliver dependable model support and reproducible performance benchmarks.
Cerințe
- Proficiency in C++ and Python programming.
- Hands-on experience bringing up and debugging machine learning models in PyTorch or a comparable framework.
- Practical MLIR experience with dialects, rewrite patterns, transformation passes, and lowering pipelines.
- Knowledge of compiler fundamentals, including intermediate representations, dataflow analysis, and code generation.
- Understanding of transformer architectures, attention mechanisms, tensor operations, and numerical precision.
- Experience profiling and optimizing workloads on GPUs or other AI accelerators.
- Ability to diagnose correctness and performance issues across model code, compiler output, kernels, and runtime execution.
- Experience with large language model inference, including GQA, sliding-window attention, mixture-of-experts, KV caching, and speculative decoding.
- Experience with FP16, BF16, FP8, or low-bit quantization and their accuracy and performance tradeoffs.
- Background developing accelerator kernels or hardware-specific compiler backends.
- Familiarity with distributed execution, model parallelism, and accelerator memory hierarchies.
- Contributions to MLIR, LLVM, inference frameworks, or related open-source projects.
Beneficii
- Stable employment in a startup-paced environment.
- Opportunities to publish and open-source advanced AI research.
- Work with one of the world's fastest AI supercomputers.
- A straightforward, non-corporate culture that respects individual beliefs.
- Ongoing learning, professional growth, and support.
- An equitable and diverse workplace.
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