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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.