What You'll Do:
Design, build, and improve machine learning models and graph/statistical algorithms — including clustering, anomaly detection, and time-series modeling and forecasting — to drive automated network optimization..
Build a real, repeatable experimentation and model-deployment workflow (e.g., using MLflow or comparable tooling), taking models from notebook to production.
Work directly with the near-real-time engineering team to identify where today's rule-based, threshold-driven decisions can be replaced by learned models that adapt to real network conditions.
Mine large-scale time-series and topology data (stored in Elasticsearch, InfluxDB, and MongoDB) to uncover patterns in network behavior at the scale of thousands of cells and large user populations.
Define and track quantitative success metrics so every model shipped can be proven to actually improve network outcomes, not just deployed and forgotten.
Present findings and roadmap recommendations to engineering and business leadership.
What We're Looking For:
5+ years of applied data science / machine learning experience, including graph algorithms, clustering, anomaly detection, or time-series modeling.
Strong Python skills; comfort working alongside Go-based production services.
Experience with large-scale time-series and document data stores (Elasticsearch, InfluxDB, MongoDB, or comparable).
Experience with ML experiment tracking and deployment tooling (MLflow or equivalent) and with streaming data pipelines (Kafka or comparable).
Excellent communication skills — the ability to turn open-ended "why is the network behaving this way" questions into a shipped, measurable model.
- Outstanding B.Sc. graduates in these fields may also be considered.
M.Sc. in Electrical Engineering, Computer Science, or Software Engineering.
Nice to Have:
Background in telecom, RF, or wireless networking (handovers, KPIs such as RSRP or PRB utilization, cell topology).
Experience turning a hand-tuned, rule-based system into a learned model running in a live production environment.
Experience with distributed or streaming compute frameworks.
Skills Required
- 5+ years of applied data science or machine learning experience
- Experience with graph algorithms, clustering, anomaly detection, or time-series modeling
- Strong Python skills
- Experience with large-scale time-series and document data stores such as Elasticsearch, InfluxDB, or MongoDB
- Experience with ML experiment tracking and deployment tooling such as MLflow
- Experience with streaming data pipelines such as Kafka
- Excellent communication skills
- M.Sc. in Electrical Engineering, Computer Science, or Software Engineering
- B.Sc. graduate in a relevant field
- Experience with telecom, RF, or wireless networking
- Experience converting rule-based systems into learned production models
- Experience with distributed or streaming compute frameworks
What We Do
At Parallel Wireless, we believe that software has the power to unleash amazing opportunities for the world. We disrupt the ways wireless networks are built and operated. We are reimagining how hardware, software and the cloud work together to change deployment economics for our customers. Our ALL G O-RAN software platform forms an open, secure and intelligent RAN architecture to deliver wireless connectivity, so all people can be connected whenever, wherever, and however they choose. We are engaged with over 50 global MNOs and have been recognized with over 74 industry awards. At the core of what we do is our team of Reimaginers who value innovation, collaboration, openness and customer success. For more information, visit: www.parallelwireless.com.
