Senior ML Infrastructure Engineer — From Pipelines to Production

Xora Innovation; San Diego (CA); Hybrid; USD 150,000 - 210,000; Full time

Job summary

Elemynt, an early-stage startup backed by Xora Innovation, is hiring for a role that builds and runs the data and ML infrastructure powering our platform. You will own data pipelines, model packaging, serving, and observability across on-premise and cloud environments.

Join a hands-on team operating in an early-stage setting, with diverse data challenges, multi-GPU workloads, and a focus on reliability, performance, and reproducible deployments.

Qualifications

• Bachelor's or Master's degree in Computer Science or a related engineering field.
• 6+ years building and shipping production software with data systems and ML infrastructure.
• Strong Python and a track record of end-to-end, reliable systems.

Responsibilities

• Build and operate data pipelines ingesting and transforming large-scale scientific output into training-ready formats.
• Develop ML data pipelines for training, fine-tuning, and RL, including curation and evaluation sets.
• Package, version, and deploy models across dev, staging, and production with registries and reproducible builds.
• Run models through CI/CD and serving workflows for batch, online, and asynchronous inference with safe rollout and rollback.
• Monitor deployed models for drift, latency, and anomalies with automated checks.
• Stand up dashboards, metrics, logs, and alerts surfacing data and model problems early.
• Design APIs, services, and internal tools to make workflows reliable and easy to use.

Skills

Python

ML infrastructure

Data pipelines

Education

Bachelor or Master in CS or related field

Tools

Docker

Kubernetes

Airflow

Dagster

Flyte

Temporal

Prometheus

Grafana

OpenTelemetry

Job description

Elemynt, an early-stage startup backed by Xora Innovation, is hiring for a role that builds and runs the data and ML infrastructure powering our platform. You will own data pipelines, model packaging, serving, and observability across on-premise and cloud environments.

Join a hands-on team operating in an early-stage setting, with diverse data challenges, multi-GPU workloads, and a focus on reliability, performance, and reproducible deployments.