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Cyera is building the security operating model for the age of AI, providing a unified control plane that secures data, access, and behaviors across humans, systems, and AI. The company is backed by leading investors and works with Fortune 1000 customers.
The ML Engineering team designs and builds infrastructure and platforms enabling researchers and engineers to develop, evaluate, deploy, and operate ML models and LLM-based features at scale. The team owns systems across the ML lifecycle—from GPU-based training and evaluation workflows to model serving, LLM infrastructure, observability, internal SDKs, and developer tooling. These platforms are used internally across teams and power ML capabilities in production services for customers.
As an ML Engineer, you will help design, build, and operate infrastructure behind machine learning and LLM systems. You'll work across the ML lifecycle, from building evaluation workflows and improving CI/CD processes to operating production ML services and evolving LLM infrastructure. You'll collaborate closely with researchers, data scientists, backend engineers, and DevOps teams to move solutions efficiently from research and experimentation into reliable production systems.
Key responsibilities include:
- Design and build ML workflows for model training, large-scale evaluation, batch prediction, and experimentation
- Develop ML infrastructure and developer tooling by automating processes and improving CI/CD and model build flows
- Operate and improve production ML services, strengthening monitoring, observability, reliability, and performance
- Deploy and operate ML workloads on Kubernetes and cloud infrastructure, including GPU-based training, evaluation, and inference
- Contribute to the LLM platform, including self-hosted model serving, LLM gateways, internal SDKs, and integrations with model providers
- Own engineering problems end-to-end from ideation through production operation
- Explore and integrate emerging technologies in ML infrastructure, LLMs, and agentic systems
The role requires a holistic engineering mindset, technical depth, ownership, curiosity, and the ability to understand problems end-to-end—from business needs and ML requirements to surrounding systems and production environments.
