Materials Data Engineer: Build AI Pipelines (Equity)

CuspAI; Cambridge; On-site; GBP 65,000 - 90,000; Full time

Benefits offered by this job

Competitive salary

Equity

Holiday entitlement

Parental leave

Professional development

Impactful work

Interdisciplinary teamwork

Job summary

CuspAI, a frontier AI company on a mission to solve breakthrough materials with AI, is seeking a Data Engineer to drive research and development. You will join a cross-disciplinary team building the infrastructure that underpins data to ML model pipelines and enable self-serve access for scientists.

As part of the role, you will design robust data pipelines for materials datasets, integrate diverse sources, ensure data quality and provenance, and collaborate with ML researchers to align data

Qualifications

• 3+ years in data engineering in scientific or research settings.
• Proficient in Python and large-scale data processing.
• Experience with workflow tools (Airflow, Prefect, Dagster).
• Strong data quality, provenance and standardisation mindset.

Responsibilities

• Design and build data pipelines for materials datasets and outputs.
• Integrate diverse data sources including databases, literature, and instruments.
• Create automated workflows for processing crystallographic data and materials properties.
• Build scalable systems for high-throughput computations and experiments.

Skills

Python

Data pipelines

Airflow

Docker

Kubernetes

CI/CD

SQL databases

Data quality

DevOps

ML data

Tools

Airflow

Prefect

Dagster

Docker

Kubernetes

Job description

CuspAI, a frontier AI company on a mission to solve breakthrough materials with AI, is seeking a Data Engineer to drive research and development. You will join a cross-disciplinary team building the infrastructure that underpins data to ML model pipelines and enable self-serve access for scientists.

As part of the role, you will design robust data pipelines for materials datasets, integrate diverse sources, ensure data quality and provenance, and collaborate with ML researchers to align data