Cerebras

More roles at Cerebras

Full Stack Machine Learning Engineer

Hybrid

Part remote, part office, you need to live within commuting distance of a named location.

Hybrid · Bengaluru, IND

Employer listed it 3 weeks ago · Added 2 weeks ago

Been open since 3 weeks ago, still being checked, but it has been live a while.

Salary

Not stated

Location

Hybrid · Bengaluru, IND

Timezone

Not stated

Contract

Full-time

Experience

Mid

What the advert says

Quoted word for word from the employer's advert. Where it says nothing, we say so.

Working hours: Not mentioned in the advert
Where you can work from: Location eligibility not confirmed
Contract: Not mentioned in the advert
Experience asked for: Not mentioned in the advert
Pay: Not mentioned in the advert

Remote flexibility

Hybrid

This role is only partly remote, the employer expects time in the office around Bengaluru, IND, Hybrid, so you need to live within commuting distance.

What the employer says

Source listing states candidate location: "Bengaluru, IND, Hybrid"
Listing mentions "Hybrid"

What Nomaders makes of it

Not suitable if you plan to move between countries

The quotes above are the employer's own words; the reading is ours. Always check the original listing and employment terms before working from another country.

Before you apply

Questions worth asking: Nomaders interpretation

This role involves office time, but the advert does not say how often. Ask how many days a week are expected on site.
No pay is stated. Ask for the salary range before investing time in interviews.
The advert does not say whether this is employment or a contractor arrangement. Ask, as it affects tax and benefits.

Produced automatically from this advert's text; not reviewed by a person. The employer's own page is the final word.

About the role

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

About the role

We are looking for an ML Engineer who can take a model from its reference implementation to efficient execution on AI accelerator hardware. You will own model bring-up, develop MLIR-based lowering paths, validate numerical correctness, and optimize performance across the model, compiler, kernels, and runtime.

This role combines practical knowledge of model architectures with strong compiler and systems engineering skills.

Responsibilities

Bring up new models: Understand model architectures, load and convert weights, implement supported execution paths, and establish correctness against reference implementations.

Lower models to hardware using MLIR: Develop and extend dialects, graph transformations, lowering passes, and hardware-specific mappings.

Support model operations: Enable attention, matrix multiplication, normalization, positional embeddings, and other operators through compiler and kernel changes.

Optimize execution: Improve operator fusion, tensor layouts, tiling, memory allocation, data movement, and parallel execution.

Optimize inference: Tune prefill and decode performance, KV cache management, batching, and quantization to improve latency, throughput, and memory efficiency.

Validate model quality: Investigate numerical differences and measure the accuracy impact of precision changes and compiler optimizations.

Diagnose bottlenecks: Use profiling, execution traces, and hardware counters to identify compute, memory, communication, and runtime limitations.

Work closely with hardware, compiler, kernel, and runtime teams to deliver reliable model support and repeatable performance benchmarks.

Required qualifications

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.

Preferred qualifications

Requirements

·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.

Benefits

No benefits package published with this listing. Ask about it at first interview.

How to apply

1Check the flexibility label above, hybrid, matches where you plan to live and work.
2Tailor your CV to the role at Cerebras, mentioning your remote working experience.
3Apply directly on the employer's careers page using the button below. Nomaders never handles your application.

Found 17d ago. Last checked today. Always confirm the details on the original posting, salary and location can change after publication.

Listing sourced from Company boards.