Graph Technologies Developers
Hiring for software engineering roles to support Graph Data Science infrastructure, for use in industrial AI at scale.
Preferred Terms: Independent contractor, up to US$200/hour depending on experience. Remote work. This could be a part-time role, e.g., for current grad students. Schedule is flexible. You must be available for some working sessions over video calls during CEST and US Pacific timezones (in English). Invoice payments via wire transfer Net-14. Terms are negotiable.
This project supports large-scale R&D and manufacturing operations within ongoing projects. Duration depends on the project; several projects run through year-end 2022. High likelihood of continuation beyond the initial contract period.
Required competencies include:
- open source integration, with emphasis on enterprise security and mitigating devops overhead
- software development in Python on Linux
- team coordination through GitHub repos, CI pipelines, container registries, and so on
- some familiarity with cloud computing and distributed systems
- use of Docker, Kubernetes
We're currently hiring for people with these specific areas of expertise:
- data labeling of open data with Rubrix
- event sourcing and storage ledger in Apache Pulsar/BookKeeper
- shared datasets in Arrow, custom Parquet formats
- using cuGraph and Dask-cuDF
- openCypher implementation
- high-performance data structures in C++
- API development in FastAPI + Pydantic (requires substantial experience in RDF, OWL, SPARQL)
- performance analysis in distributed systems
We do not expect candidates to have experience in each of the technology areas listed above and will train on-the-job as needed. That said, priority will be given to candidates who have experience which covers more of the listed skills.
Big plus if you have prior experience working with distributed graph algorithms at scale.
Additional Details: For more details, check:
- "Graph Thinking" article on Medium
- Python open source
kglabpublic repo on GitHub
- Hardware > Software > Process: Data Science in a Post-Moore's Law World by NVIDIA
- Graph Data Science LinkedIn group
This is a great position if you'd like to get mentoring from experts in these technologies, plus hands-on experience overall in industrial AI applications.
Next Steps: Send email to email@example.com with your profile links on GitHub, StackOverflow, and other communities of practice. Please include any related articles, tutorials, conference/meetup talk videos, or other portfolio materials. We'll schedule a video call.
PRINCIPALS ONLY; WE DO NOT REPLY TO CORRESPONDENCE FROM RECRUITERS OR OUTSOURCING FIRMS