Senior Data & ML Infrastructure Engineer (Xora Portfolio Company)

Xora Innovation is an early-stage startup focused on developing applied intelligence to bring AI into the real world. The Senior Data & ML Infrastructure Engineer will build and operate the data and machine-learning infrastructure for the platform, ensuring efficient data pipelines and model deployment in production environments.

Responsibilities:

• 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

Requirements:

• 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
• 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