Machine Learning Engineer – Model Bring-Up

Cerebras

. Bring up new models by understanding architectures, loading and converting weights, implementing supported execution paths, and establishing correctness against reference implementations .

Posted 10/9/2026full-timeBengaluru • IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in optimizing machine learning models through advanced programming in C++ and Python, with a strong focus on MLIR and performance tuning for AI accelerators. Proficient in debugging and profiling to ensure model correctness and efficiency across various execution environments.

Highest-signal resume keywords

C++ ProgrammingPython ProgrammingMLIR ExperienceModel OptimizationGPU Profiling

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Hard Skills

Machine Learning Model DebuggingTransformer ArchitecturesQuantization TechniquesCompiler FundamentalsTensor OperationsAttention MechanismsPerformance BenchmarkingKernel DevelopmentDataflow AnalysisIntermediate Representations

Tools & Technologies

PyTorchLLVMAI AcceleratorsDistributed ExecutionModel Parallelism

Industry Keywords

MLIR DialectsOperator FusionKV CachingSpeculative DecodingLow-Bit Quantization

Tech Stack

Tools & technologies

PythonPyTorchC++

About the role

Key responsibilities & impact

Bring up new models by understanding architectures, loading and converting weights, implementing supported execution paths, and establishing correctness against reference implementations
Lower models to hardware using MLIR by developing and extending dialects, graph transformations, lowering passes, and hardware-specific mappings
Enable attention, matrix multiplication, normalization, positional embeddings, and other operators through compiler and kernel changes
Optimize operator fusion, tensor layouts, tiling, memory allocation, data movement, and parallel execution
Tune prefill and decode performance, KV cache management, batching, and quantization to improve latency, throughput, and memory efficiency
Investigate numerical differences and measure accuracy impact of precision changes and compiler optimizations
Use profiling, execution traces, and hardware counters to identify compute, memory, communication, and runtime limitations
Collaborate with hardware, compiler, kernel, and runtime teams to deliver reliable model support and repeatable performance benchmarks

Requirements

What you’ll need

Strong programming skills in C++ and Python
Hands-on experience bringing up and debugging ML models in PyTorch or a comparable framework
Practical experience with MLIR, including dialects, rewrite patterns, transformation passes, and lowering pipelines
Understanding 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 debug correctness and performance issues across model code, compiler-generated code, kernels, and runtime execution
Experience with LLM inference, including GQA, sliding-window attention, MoE, KV caching, and speculative decoding
Experience with FP16, BF16, FP8, or low-bit quantization and their accuracy and performance tradeoffs
Experience 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

Comp & perks

Job stability with startup vitality
Opportunity to publish and open source cutting-edge AI research
Work on one of the fastest AI supercomputers in the world
Simple, non-corporate work culture that respects individual beliefs
Continuous learning, growth and support
Equal and diverse work environment