Data Scientist for READINESS Autonomous Materials Synthesis Laboratory

Job Description

The Rice University Department of Computer Science and Department
of Materials Science and NanoEngineering are seeking a Data
Scientist to build and operate the data infrastructure for
READINESS, a new $20 million NSF-funded project transforming four
materials synthesis systems (CVD, thermal, PVD, and plasma CVD
reactors for 2D materials, oxides, and diamond films) into an
AI-driven, remotely accessible autonomous laboratory.

The READINESS facility will generate substantial and diverse data,
including growth recipes, reactor time-series, user interactions,
optical, SEM/TEM and AFM images, spectra, and continuous robot
telemetry. The Data Scientist will be responsible for developing
and maintaining the central data lake that captures, indexes,
secures, and serves this information across the project.

The position combines data engineering and analysis, including
deploying an S3-compatible object store, developing ingestion
connectors for live instruments and robot controllers, and
establishing SQL and vector-search capabilities under Rice single
sign-on. The role will also compute embeddings over images and
spectra, develop retrieval and similarity-search services, and
build AI agents capable of answering researchers' questions
directly from the data.

Reporting to the faculty leads of the READINESS Data Infrastructure
working group, the Data Scientist will collaborate closely with the
synthesis platform teams, robotics and AI/digital-twin working
groups, Rice IT and Information Security, and graduate and
undergraduate students involved in the project.

This is a hands-on computer science and data engineering position
that requires substantial ownership of the project's central data
infrastructure. A background in materials science, chemistry, or
laboratory science is not required ; domain-specific knowledge
can be developed through collaboration with the project's
scientific experts. Working within a small team, the Data Scientist
will develop the foundational systems through which data generated
across the READINESS facility is captured, organized, and made
accessible.

Special Instructions to Applicants:

Applicants must submit a CV, contact information for three
professional references, and responses to three required
supplemental application questions. These questions are specific to
the position and must be completed as part of the
application.

Applications that do not include the required responses may be
considered incomplete.

Ideal Candidate Statement:

The ideal candidate is a skilled software or data engineer who
enjoys collaborating across disciplines and brings experience
designing data models and developing reliable systems to capture,
organize, and serve complex data. Equally comfortable deploying
storage infrastructure, troubleshooting connections to physical
instruments, training embedding models, and integrating LLM agents
with databases, this individual has experience developing and
deploying data systems that others rely on. An appreciation for the
challenges and opportunities presented by diverse, real-world data
generated by scientific equipment is essential, along with careful
attention to system reliability, documentation, and data
integrity.

Self-directed and pragmatic, they can translate a project goal into
a working technical solution, selecting appropriate tools and
collaborating with the researchers who operate the instruments to
ensure data is captured accurately and consistently with the
necessary metadata. They communicate effectively with colleagues
from different technical backgrounds and understand the importance
of security, provenance, and access control in a
multi-institutional research environment.

Workplace Requirements:

This position is fully remote, permitting all tasks to be completed
from any location within the United States. Working hours will
remain central standard time. Per Rice policy 440, work
arrangements may be subject to change.

Hiring Range: Up to $105,000 annually.

This is a one-year term limited, benefits-eligible position funded
by a grant, soft and/or restricted funds, and may be renewed based
on continued funding availability, research needs, and
performance.

Minimum Requirements:

• Bachelor’s degree in computer science, data science,

engineering, or a related quantitative field.

• Three or more (3+) years of related professional experience in

data engineering, data science, software engineering, or research
computing.

Skills:

• Strong programming skills and ability to write

production-quality, tested, documented code.

• Proficiency with SQL and relational data modeling, including

scientific or operational database schemas.

• Experience with object storage (S3, Ceph, or MinIO), columnar

formats (Parquet), and distributed query engines (Trino, Presto,
Spark, or similar).

• Working knowledge of Linux administration, containers

(Docker/Kubernetes), and at least one major cloud platform (AWS or
Azure).

• Familiarity with PyTorch, embedding models, vector databases,

and retrieval-augmented or agentic LLM applications.

• Understanding of authentication and access controls

(SSO/SAML/OIDC, role-based access, audit logging) and secure
research-data handling.

• Ability to connect software to physical instruments and devices

using network or file-based protocols; ROS/rosbag experience is a
plus.

• Ability to learn an unfamiliar scientific domain, translate

collaborators’ requirements into working systems, manage competing
requests, and communicate clearly.

Preferences:

• Five or more years of professional experience building and

operating data systems.

• Experience building data pipelines for scientific instruments,

laboratory automation, manufacturing, or IoT/telemetry.

• Experience operating Ceph or comparable software-defined

storage, or managing cloud object-storage tenancies at 100 TB+
scale.

• Experience with GPU-accelerated similarity search (FAISS,

Milvus, Qdrant) and serving open-weight LLMs.

• Experience in academic research or national laboratories,

including working with institutional IT and security offices.

• Experience monitoring production data systems and implementing

backup and recovery.

Essential Functions:

• Designs, deploys, and operates the READINESS central data lake,

including the S3-compatible object store (self-hosted Ceph or
cloud), bucket layout, versioning, and access policies.

• Develops and maintains ingestion connectors that capture data

unattended from synthesis tools, characterization instruments, and
robotic systems, and land it in the store with structured,
de-identified metadata and provenance links.

• Designs and maintains the project's metadata and provenance

schema and the Python client library that all project teams use to
read and write data.

• Deploys and administers the SQL query layer (Trino and Hive

Metastore over Parquet) and programmatic APIs for researchers, the
user portal, and digital-twin and AI teams.

• Builds embedding pipelines and GPU-resident vector indexes over

images, spectra, and logs, and exposes similarity-search and
retrieval-augmented-generation services.

• Develops and demonstrates AI agents that answer questions about

experiments by retrieving from the data lake, in collaboration with
the AI & Digital Twins working group.

• Implements and maintains security controls, including Rice

single sign-on with MFA, tiered role-based access, encryption, and
immutable audit logging, and works with Rice Information Security
and Research Security on design reviews and compliance.

• Monitors system health, performs integrity checks and backups,

and documents and tests recovery procedures.

• Gathers requirements from synthesis platform, characterization,

and robotics teams; attends platform team and working-group
meetings; and sets node-wide standards for data formats, metadata,
and access.

• Writes documentation, user guides, and onboarding materials,

and trains researchers and partner-site collaborators to use the
data infrastructure.

• Mentors graduate and undergraduate students working on data

infrastructure projects.

• Performs all other related duties as assigned

Rice University HR | Benefits: https://knowledgecafe.rice.edu/benefits

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