Senior Data & ML Infrastructure Engineer (Xora Portfolio Company

ABOUT ELEMYNT

ELEMYNT is an early-stage startup built by Xora Innovation. We develop applied intelligence that brings AI into the real world. Our platform combines advanced machine learning, high-performance simulation, and modern software engineering to accelerate the design, validation, and deployment of new materials. Our work sits at the intersection of AI, physics, and large-scale computation. The problems are hard, the stakes are high, and the impact is tangible.

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.

• 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.

• 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, Open Telemetry, 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…