Part-Time Assistant Programmer

Penn State University; University Park (TX); Remote; USD 60,000 - 80,000; Part time

Job summary

A major educational institution is seeking a part-time programmer to develop ETL pipelines and join a dynamic team focused on NLP and literature-based recommendations. The position is fully remote within the U.S. and offers $57/hour with 20 hours per week. Ideal candidates will have a strong background in Python and ML/NLP. Apply with a cover letter and resume.

Qualifications

• 3+ years of Python and ML/NLP engineering experience.
• Strong engineering hygiene including tests and documentation.
• Clear communication skills in a remote team context.

Responsibilities

• Develop an ETL pipeline to manage peer-reviewed articles.
• Fine-tune transformer models for relation extraction.
• Build and maintain a knowledge graph linked to source papers.

Skills

Python

NLP/ML

ETL processes

Data manipulation (pandas)

APIs development

Tools

PyTorch

TensorFlow

Postgres

Neo4j

Job description

APPLICATION INSTRUCTIONS

• CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process. Please do not apply here, apply internally through Workday.
• CURRENT PENN STATE STUDENT (not employed previously at the university) and seeking employment with Penn State, please login to Workday to complete the student application process. Please do not apply here, apply internally through Workday.
• IF YOU ARE NOT a current employee or student, please click “Apply” and complete the application process for external applicants.

Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants.

JOB DESCRIPTION AND POSITION REQUIREMENTS

The Department of Computer Science and Engineering within the College of Engineering is seeking applicants for a part-time programmer.

The role: Help us build a focused, sharp system that turns peer-reviewed nutrition literature into transparent, evidence-based recommendations. You’ll implement the Extract, Transform, Load (ETL) pipeline from OpenAlex, fine-tune and wire up transformer models for relation extraction, and ship a production-grade recommendation/generative layer backed by a provenance-rich knowledge graph.

What you’ll do:

• Stand up and own a PubMed or OpenAlex ETL that ingests open-access articles, normalizes metadata, and keeps our corpus fresh.
• Use and/or fine tune transformer models (e.g., BERT variants) to extract semantic triples (like: (ingredient)–[biolink:ameliorates_condition]–>(health condition)); build evaluation and error-analysis loops.
• Implement a ranking layer that blends evidence strength (study design, sample size, effect size) with model confidence.
• Build the recommendation/generative service that balances constraints (evidence score, compatibility, regulatory limits) and exposes a clean API.
• Construct/maintain a knowledge graph that links every recommendation back to source papers (full provenance) and supports plain-language evidence summaries.
• Collaborate tightly with PI and domain scientists; deliver milestones on a fast, 12-month pilot timeline.

Core stack we use (and you’re comfortable with):

• Python 3.x; modern NLP/ML (PyTorch or TensorFlow, Hugging Face, scikit-learn); data/ETL tooling (pandas, spaCy, Pydantic, Airflow/Prefect).
• APIs/services: RESTful design, auth, pagination, retries, structured logging, unit/integration tests, CI.
• Storage/search: Postgres + vector/embedding store; graph DB experience welcome.
• DevOps basics: containers, reproducible envs, simple local deploys, secrets management.

Nice to have:

• Deep NLP experience (relation extraction, weak supervision, prompt/adapter tuning).
• Semantic KGs (RDF/OWL, Neo4j/graph tooling), ontology work, and/or the Biolink Model.
• Experience with biomedical text corpora and literature mining.
• You’ve shipped scrappy, reliable research-to-prod systems before.

What you bring:

• 3+ years professional Python and ML/NLP engineering, or equivalent portfolio.
• Strong engineering hygiene (tests, docs, code review) and product sense.
• Clear, direct communication in a remote team.
• Bonus: prior work at the intersection of ML + biosciences/clinical data.

Logistics & pay:

• Fully remote (U.S.); office space available at Penn State for hybrid/in-person if preferred.
• Part-time at $57/hour, 20 hours/week, 50 weeks. Continuation beyond 50 weeks depends on renewal/next-round funding.
• Please submit a brief cover letter and resume/CV.
• For more information on restrictions for part-time remote employment locations: Notice to Out of State Applicants.

BACKGROUND CHECKS/CLEARANCES

Employment with the University will require successful completion of background check(s) in accordance with University policies.

CAMPUS SECURITY CRIME STATISTICS

Pursuant to the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act and the Pennsylvania Act of 1988, Penn State publishes a combined Annual Security and Annual Fire Safety Report (ASR). The ASR includes crime statistics and institutional policies concerning campus security, such as those concerning alcohol and drug use, crime prevention, the reporting of crimes, sexual assault, and other matters. The ASR is available for review here.

EEO IS THE LAW

Penn State is an equal opportunity employer and is committed to providing employment opportunities to all qualified applicants without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. If you are unable to use our online application process due to an impairment or disability, please contact 814-865-1473.

The Pennsylvania State University is committed to and accountable for advancing equity, respect, and belonging. We embrace individual uniqueness, as well as a culture of belonging that supports equity initiatives, leverages the educational and institutional benefits of inclusion in society, and provides opportunities for engagement intended to help all members of the community thrive. We value belonging as a core strength and an essential element of the university’s teaching, research, and service mission.

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