layiq
/la·yiq/adjective
worthy; deserving; fitting; suitable.
A role, opportunity, or path that merits attention, time, and pursuit.
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Job opportunity
ML Infrastructure Engineer
Clera
San Mateo, California, United States
Source: Clera careers · View original posting
- Sponsorship
- On-site in San Mateo, California, United States. Visa sponsorship is not available for this role.
Employer job description
From Clera's posting. “We” and “our” refer to the employer.
About the Role
This is a hands-on ML Infrastructure Engineer role at an early-stage enterprise AI company building a context and data governance layer that makes AI agents reliable in production. You will own the inference and model-serving infrastructure end to end, ensuring agents run fast and reliably at increasing concurrency. The work is squarely production-focused with real-world impact across regulated industries like insurance, banking, healthcare, and asset management.
What You'll Do
- Design, build, and scale inference and model-serving infrastructure from the ground up through production deployment.
- Optimize systems for latency, throughput, and reliability under high concurrency.
- Collaborate closely with ML and infrastructure teams to ensure seamless integration and surface performance bottlenecks.
- Drive solutions to infrastructure challenges across a fast-moving, cross-functional team.
What We're Looking For
- 5 or more years building and operating machine learning inference systems, model-serving platforms, or ML infrastructure in production environments.
- Hands-on experience designing and scaling inference-serving systems using tools such as TensorFlow Serving, TorchServe, Triton, KServe, or equivalent custom solutions.
- Strong distributed systems fundamentals, including containerization and orchestration with Docker and Kubernetes.
- Proficiency with monitoring and observability tooling for production systems, such as Prometheus, Grafana, or distributed tracing frameworks.
- Experience deploying and managing ML workloads on cloud platforms (AWS, GCP, or Azure).
- Proficiency in at least one systems or backend language: Python, Go, Rust, C++, or Java.
- Comfort collaborating across both ML and infrastructure disciplines in a fast-paced environment.
- Nice to have: experience with knowledge graphs, semantic search, or graph databases; real-time or low-latency inference systems; agentic or multi-step AI pipelines; enterprise data integration or pipeline infrastructure.