Cloud and Systems Integration Developer
Maxon
Build Azure infrastructure and enterprise integrations supporting Maxon’s creative software operations. Automate deployments, identity workflows, and system connections across the organization.
Open
Cerebras develops wafer-scale AI hardware and integrated software for high-performance model training and inference. Its Wafer-Scale Engine powers the CS-3 system and Cerebras Inference Cloud, which supports OpenAI API-compatible workflows and deployment across on-premise, cloud, and edge environments. The company’s platform is designed for applications including model serving, fine-tuning, pre-training, real-time AI agents, genomics, drug discovery, cybersecurity, and enterprise search. Cerebras brings together specialized chips, systems, and software for organizations building and operating demanding artificial intelligence workloads, with a team of 501–1,000 employees.
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.
Description
- 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.
Requirements
- 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.
Benefits
- 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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