Senior Data ML Infrastructure Engineer (Xora Portfolio Company)

Role overview

Responsibilities

• check_circle Build the data pipelines that ingest, transform, and curate large-scale scientific output into efficient, training-ready formats on object storage.
• check_circle Make that data fast to query and cheap to reuse, so analysis and downstream jobs aren’t left waiting on it.
• check_circle Build the ML data pipelines for training, fine-tuning, and reinforcement learning: curation, deduplication, formatting, and the evaluation sets that keep training honest.
• check_circle Catch bad data early, with validation and quality gates that check schema, distribution, and completeness before it reaches a model.
• check_circle Package, version, and deploy models across development, staging, and production, with registries and reproducible builds that keep every deployment traceable.
• check_circle Run models through CI/CD and serving workflows for batch, online, and asynchronous inference, with safe rollout, rollback, and quick diagnosis when something breaks.
• check_circle Monitor deployed models for drift, degradation, latency, and anomalies, with automated regression checks that flag trouble before users do.
• check_circle Stand up dashboards, metrics, logs, and alerts that surface data and model problems while they’re still small.
• check_circle Design the APIs, services, and internal tools that make these workflows reliable and easy for engineers and scientists to use.
• check_circle 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.
• check_circle Strong Python, and a track record of shipping reliable systems end to end that other people end up depending on.
• check_circle Hands-on experience with large-scale data systems: object storage, efficient columnar and array data formats, and distributed query and compute engines.
• check_circle 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.
• check_circle Production MLOps experience: packaging, versioning, serving, and monitoring models for drift, latency, and anomalies, backed by model registries and CI/CD for ML.
• check_circle Deep hands-on experience with containers and orchestration (Docker, Kubernetes) and workflow orchestrators such as Airflow, Dagster, Flyte, or Temporal.
• check_circle Experience instrumenting production systems and using their telemetry, logs, and metrics (Prometheus, Grafana, OpenTelemetry, or similar) to debug real incidents.
• check_circle 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.

Preferred qualifications

• 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.

About the company