# Technical Lead, Artificial Intelligence at Parallel Wireless

> Location unclear · Staff · Other · open. Source: https://alderhq.app/jobs/lever%3Aparallelwireless%3Ad1f8165d-a13b-47d4-b4e4-9c22d12cd6ba (read 2026-11-04).

## Facts (observed)

- Company: [Parallel Wireless](https://alderhq.app/companies/parallel-wireless)
- Location text: Kfar Saba
- Where it can be done from (inferred): Location unclear (evidence: "Location: Kfar Saba")
- Posted: 2026-09-15 (50 days ago)
- First seen by alder: 2026-11-03 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.lever.co/parallelwireless/d1f8165d-a13b-47d4-b4e4-9c22d12cd6ba
- Skills listed (inferred): Python, Docker, PyTorch, LLMs

## Pay and sponsorship

- Posted pay: not posted

## Description (the employer's text)

We are building the AI layer of the Radio Access Network, applying agentic AI, machine learning, and LLMs to real RAN challenges — from network operations and root cause analysis to PHY simulation and uplink link adaptation.

We are looking for a Technical Lead to own the end-to-end architecture and technical direction of these AI systems. This is a hands-on senior individual contributor role, combining architecture, coding, technical leadership, and mentorship.

You will work from our Kfar Saba site alongside RAN, PHY, and software teams, building production-grade AI solutions for real cellular networks.

### What you'll do:

- Define the end-to-end architecture for AI/ML systems, including agent orchestration, model serving, data pipelines, RAG, and evaluation infrastructure.
- Drive technical decisions around models, frameworks, deployment strategies, and build-vs-buy approaches.
- Prototype and implement critical components while setting engineering and code-quality standards.
- Lead AI solutions from prototype to production, including CI/CD, monitoring, model lifecycle, versioning, and rollback.
- Establish evaluation frameworks, benchmarks, and safety criteria for AI systems operating on network data.
- Mentor engineers through architecture discussions, design reviews, and code reviews.
- Collaborate with RAN Systems, PHY, L2/L3, Product, and customer-facing teams to translate network challenges into practical AI/ML solutions.
- Contribute to technical roadmap discussions and customer-facing architecture discussions.
### What you should have:

- 7+ years of experience in software or ML engineering, with significant experience delivering production systems.
- Proven technical leadership and experience owning the architecture of complex systems end to end.
- Strong Python skills and hands-on experience with PyTorch or similar ML frameworks.
- Practical experience with LLM-based systems, including agents, tool calling, RAG, orchestration, prompt/context engineering, and evaluation.
- Strong understanding of classical ML, including time-series analysis, anomaly detection, and supervised learning.
- Working knowledge of 4G/5G RAN architecture, L1/L2/L3, network KPIs, and cellular network operations.
- Experience with MLOps, containers, CI/CD, experiment tracking, model monitoring, and production deployment.
- Excellent English and strong technical communication skills.
### Nice to have:

- Hands-on experience in RAN, wireless infrastructure, telecom operators, or chipset companies.
- Knowledge of O-RAN, RIC, rApps/xApps, and E2/A1/O1 interfaces.
- Experience with link adaptation, scheduling, RRM, channel modeling, or PHY simulation.
- Experience with reinforcement learning or contextual bandits for real-world control problems.
- Experience deploying ML models in real-time or resource-constrained environments.
- Background in signal processing, communications, or information theory.
- M.Sc. / Ph.D. in Computer Science, Electrical Engineering, Applied Mathematics, or a related field.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/lever%3Aparallelwireless%3Ad1f8165d-a13b-47d4-b4e4-9c22d12cd6ba and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.