ML Engineer

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs.

Cerebras' current customers include global corporations across multiple industries, national labs, and top-tier healthcare systems. In January, we announced a multi-year, multi-million-dollar partnership with Mayo Clinic, underscoring our commitment to transforming AI applications across various fields. In August, we launched Cerebras Inference, the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services.

About The Role

We are looking for a versatile and experienced ML Engineer to join our AI/ML engineering team. In this role, you will be responsible for designing and building robust data pipelines, and deploying machine learning models efficiently into production environments. The ideal candidate has strong Python programming skills, hands-on experience with cloud platforms (AWS, GCP, or Azure), and understands the lifecycle of ML inference services.

Responsibilities

• Design, build, and maintain scalable data curation and transformation pipelines for various tasks from coding to reasoning.
• Ensure data quality, reproducibility, and security across the pipeline lifecycle.
• Design, develop, and maintain cloud-based inference services (e.g AWS based custom Kubernetes deployments).
• Containerize ML models using Docker and orchestrate deployments via Kubernetes.
• Build automated CI/CD pipelines for deploying and updating models.
• Set up monitoring, logging, and alerting for model health, latency, and drift using tools like Prometheus, Grafana, OpenTelemetry, or cloud-native observability stacks.
• Integrate model versioning and rollback mechanisms for safe experimentation and updates.
• Package and deploy machine learning inference services using frameworks like FastAPI, TorchServe, Triton Inference Server, etc.
• Collaborate with data scientists and ML engineers to ensure smooth integration from model development to production.

Skills And Qualifications

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, Data Engineering, or related technical field.
• 3 years of experience in building data pipelines and deploying ML models.
• Proficiency in Python and relevant libraries (Pandas, NumPy, PyTorch/TensorFlow, Hugging face, etc.).
• Experience writing efficient SQL queries for data extraction, transformation, and aggregation.
• Knowledge of distributed and heterogenous computing across CPU/GPU using frameworks like slurm.
• Solid understanding of REST APIs, containerization (Docker), and service deployment frameworks.
• Experience with at least one cloud platform: AWS, GCP, or Azure.
• Hands-on with DevOps practices for deploying and monitoring production systems.
• Familiarity with CI/CD tools (e.g., GitHub Actions, Jenkins, Argo).
• Strong communication skills and the ability to work cross-functionally.

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:

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

Read our blog: Five Reasons to Join Cerebras in 2025.

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 to review our CCPA disclosure notice.

---