---
title: 'Full Stack Machine Learning Engineer at Cerebras Systems'
canonical: 'https://feeny.ai/job/full-stack-machine-learning-engineer-cerebras-systems-bengaluru-fm2j4tvccywm'
type: 'job'
last_seen: '2026-10-20'
---

# Full Stack Machine Learning Engineer at Cerebras Systems

- **Company:** [Cerebras Systems](https://feeny.ai/companies/cerebras-systems)
- **Location:** Bengaluru, India
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-10-09
- **Last confirmed live:** 2026-10-20
- **Apply:** https://jobs.ashbyhq.com/cerebras/c2198d42-ccad-4bee-aa43-83027aa5e46e

## Job description

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](https://openai.com/index/cerebras-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. 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

- 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.

## Why Join Cerebras

People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:

- Build a breakthrough AI platform beyond the constraints of the GPU.
- Publish and open source their cutting-edge AI research.
- Work on one of the fastest AI supercomputers in the world.
- Enjoy job stability with startup vitality.
- Our simple, non-corporate work culture that respects individual beliefs.

Find out more about what it's like to work at Cerebras [here](https://www.cerebras.ai/join-us)! Apply today and become part of the forefront of groundbreaking advancements in AI! Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them. This website or its third-party tools process personal data. For more details, click [here](https://www.cerebras.net/privacy/) to review our CCPA disclosure notice.

## About Cerebras Systems

## Company Overview
- **One-liner**: Cerebras builds wafer-scale AI chips (the largest semiconductors ever made) and supercomputers that deliver up to 15x faster inference than GPUs, offered both on-premises and via cloud APIs.
- **Entity Type**: Public (NASDAQ: CBRS) — filed for IPO and now listed on the Nasdaq.
- **Headquarters**: Sunnyvale, California, United States
- **Founded**: 2015
- **Founders**: Andrew Feldman, Gary Lauterbach, Michael James, Sean Lie, Jean-Philippe Fricker

## Core Business
- **Primary industries**: Semiconductor manufacturing, AI computing hardware and software, cloud AI services.
- **Target customers**: B2B – enterprises, government research labs, cloud service providers, and AI developers.
- **Mission**: “Building impossible technologies so you can too” – accelerating AI through wafer-scale computing.

## Products & Services
- **Wafer-Scale Engine (WSE-3)**: The world’s largest AI chip (215 mm²), 58x larger than a typical GPU, built for ultra‑fast AI training and inference. Manufactured by TSMC.
- **CS-3 Supercomputer**: A single system powered by the WSE-3, delivering record-breaking AI performance for on-premises deployment.
- **Cerebras AI Inference Cloud & Training Cloud**: Pay-as-you‑go APIs that provide drop‑in OpenAI‑compatible access to Cerebras’ hardware for serving, fine‑tuning, and pre‑training models.
- **Condor Galaxy Network**: A series of supercomputers (e.g., CG‑1 with 4 exaFLOPs) built in partnership with G42 for high‑performance AI.

## Market Standing
- **Valuation/Market Cap**: Not disclosed (recently public; market cap data not available in provided sources).
- **Key Metric**: Annual revenue of $78.7 M (latest); total funding of $2.7 B across 15 rounds.
- **Notable Investors/Partners**: G42, OpenAI (signed in 2026), Amazon Web Services (signed in 2026), Meta (Llama API), Perplexity, Mistral, Hugging Face, OpenRouter.
- **Growth Signals**: 
  - 34.3% YoY headcount growth (701 employees).
  - Rapid data center expansion across North America and Europe.
  - Won the HPCwire Readers’ & Editors’ Choice 2025 for Best AI Product/Technology.
  - Named to Forbes America’s Best Startup Employers 2026 and Fast Company Most Innovative Companies 2026 (AI).

## Competitive Advantages
- **Wafer‑scale architecture**: Eliminates interconnect bottlenecks, reducing latency and delivering up to 15x faster inference than GPU clusters.
- **Largest AI chip ever built**: 58x larger than any GPU, enabling massive on‑chip memory (SRAM) and compute density.
- **Full‑stack offering**: Hardware + cloud APIs + on‑prem systems, giving customers deployment flexibility.
- **Strong customer relationships**: Key contracts with OpenAI, AWS, G42, and Mayo Clinic (2024 Gordon Bell Prize work).

## Strategic Focus
- **Scale inference capacity**: Building out data centers to become the world’s #1 provider of high‑speed AI inference.
- **Deepen cloud partnerships**: Expanding pay‑as‑you‑go cloud access and enterprise deployments.
- **Advance wafer‑scale technology**: Continuous R&D on WSE‑3 successors and software stack (compiler, ML workflows).
- **Broaden model support**: Already serving Llama, Gemma, Qwen, Mistral, and more; integrating with Hugging Face and OpenRouter.

## Why Work Here
- **Culture**: “Extraordinary people, breakthrough innovation, global impact” – the company highlights collaborative, low‑overhead teams with little bureaucracy.
- **Work environment**: Hybrid/office with locations in Sunnyvale, San Diego, Toronto, and Bangalore. Inclusive and flexible policy.
- **Perks**: Premium medical/dental/vision, life insurance, generous vacation, 401(k) and Group RRSP retirement plans, daily catered meals, healthy snacks, family‑friendly events (including CEO’s famous BBQ).
- **Engineering focus**: Tackling fundamental challenges in chip design, system software, compiler technology, and ML workflows. Positions range from hardware (Design Verification, Manufacturing) to software (SRE, Compiler, Cloud).
- **Philanthrophy**: Supports local communities and hosts students from around the world for Q&A.

## Sources
1. [cerebras.ai/company](https://www.cerebras.ai/company)
2. [cerebras.ai](https://www.cerebras.ai/)
3. [cerebras.ai/join-us](https://www.cerebras.ai/join-us)
4. [linkedin.com/company/cerebras-systems](https://www.linkedin.com/company/cerebras-systems)

## Other roles at Cerebras Systems

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- [AI Inference Core - Senior SDET, Release Integration Testing](https://feeny.ai/job/ai-inference-core-senior-sdet-release-integration-testing-cerebras-systems-k4pm4rc616mx) — Sunnyvale, CA
- [Senior Manager, Service Operations & Enablement](https://feeny.ai/job/senior-manager-service-operations-enablement-cerebras-systems-sunnyvale-qmxx1hpzvnvq) — Sunnyvale, CA
- [Supply Chain Program Manager](https://feeny.ai/job/supply-chain-program-manager-cerebras-systems-sunnyvale-5yqqr6hwg3zr) — Sunnyvale, CA
- [SEC Reporting & Technical Accounting Manager](https://feeny.ai/job/sec-reporting-technical-accounting-manager-cerebras-systems-sunnyvale-g6g9t52gcjn6) — Sunnyvale, CA
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- [Head of DevRel](https://feeny.ai/job/head-of-devrel-cerebras-systems-sunnyvale-2ahvwfsv5ame) — Sunnyvale, CA
- [Cluster Datacenter Manager](https://feeny.ai/job/cluster-datacenter-manager-cerebras-systems-united-states-alabama-xx25gw65zsh2) — United States / Alabama