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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data & ML Infrastructure Engineer (Xora Portfolio Company) - **Company:** XORA INNOVATION USA INC. - **Location:** San Diego, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Airflow, Big Data, Databases, Continuous Integration, Data Deduplication, Data Infrastructure, Data Systems, Software Debugging, Python (Programming Language), Machine Learning, Open Source Technology, Prometheus, Management of Software Versions, Reinforcement Learning, Grafana, Generative AI, Kubernetes, Information Technology, Machine Learning Operations, Data Pipelines, Docker - **Published:** August 4, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=2e480ed3cd09ac3f ## About the Role * Bachelor's or Master's degree in Computer Science or a related engineering field, and 6+ years building and shipping production software, with real depth across data systems and ML infrastructure. * Strong Python, and a track record of shipping reliable systems end to end that other people end up depending on. * Hands-on experience with large-scale data systems: object storage, efficient columnar and array data formats, and distributed query and compute engines. * Experience building data and ML data pipelines: ingestion, transformation, curation, and the validation and quality gates that catch problems before they reach training or inference. * Production MLOps experience: packaging, versioning, serving, and monitoring models for drift, latency, and anomalies, backed by model registries and CI/CD for ML. * Deep hands-on experience with containers and orchestration (Docker, Kubernetes) and workflow orchestrators such as Airflow, Dagster, Flyte, or Temporal. * Experience instrumenting production systems and using their telemetry, logs, and metrics (Prometheus, Grafana, OpenTelemetry, or similar) to debug real incidents. * Comfort working across cloud and HPC, including distributed multi-GPU, and owning ambiguous systems end to end in an early-stage setting with little scaffolding. NICE TO HAVE * Data-quality and governance tooling such as Great Expectations or Evidently, plus data contracts, lineage, metadata catalogs, or reproducibility tooling. * Model-serving patterns for high-throughput or asynchronous inference, and runtime uncertainty or out-of-distribution monitoring. * Experiment-tracking and model-lifecycle tooling such as MLflow or Weights & Biases, or serving stacks such as Ray Serve, KServe, or Kubeflow. * Experience applying ML to scientific data, such as property prediction, generative models, or graph-based approaches. * It'd be a plus if you've worked with atomistic-ML data tooling: Atompack, ASE-style structure databases, extended-XYZ datasets, or the large public corpora built on them. * Contributions to open-source ML or data infrastructure. ## Description This role builds and operates the data and machine-learning infrastructure the platform runs on: the pipelines that turn large-scale scientific output into data models can train on, and the systems that move those models from research into production and keep them running there. Hands-on work, close to both the data and the models. You'll own both sides. On the data side, that's pipelines and formats that keep large-scale output fast to query and ready for training. On the model side, it's the packaging, serving, monitoring, and CI/CD that let models ship safely and stay healthy once they're live. And because the platform runs inside customers' own secure environments, on their clusters, in their cloud, or a mix of the two, whatever you build has to stay observable and reliable in places you don't operate. Everything downstream depends on this layer. When it's slow or unreliable, so is everything built on top of it. WHAT YOU WILL DO * Build the data pipelines that ingest, transform, and curate large-scale scientific output into efficient, training-ready formats on object storage. * Make that data fast to query and cheap to reuse, so analysis and downstream jobs aren't left waiting on it. * Build the ML data pipelines for training, fine-tuning, and reinforcement learning: curation, deduplication, formatting, and the evaluation sets that keep training honest. * Catch bad data early, with validation and quality gates that check schema, distribution, and completeness before it reaches a model. * Package, version, and deploy models across development, staging, and production, with registries and reproducible builds that keep every deployment traceable. * Run models through CI/CD and serving workflows for batch, online, and asynchronous inference, with safe rollout, rollback, and quick diagnosis when something breaks. * Monitor deployed models for drift, degradation, latency, and anomalies, with automated regression checks that flag trouble before users do. * Stand up dashboards, metrics, logs, and alerts that surface data and model problems while they're still small. * Design the APIs, services, and internal tools that make these workflows reliable and easy for engineers and scientists to use. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [5 steps for running a Kubernetes environment at scale](https://www.wearedevelopers.com/videos/88-5-steps-for-running-a-kubernetes-environment-at-scale) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [The state of MLOps - machine learning in production at enterprise scale](https://www.wearedevelopers.com/videos/369-the-state-of-mlops-machine-learning-in-production-at-enterprise-scale) - [All your telemetry data from any source in one place](https://www.wearedevelopers.com/videos/57-all-your-telemetry-data-from-any-source-in-one-place) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)