Senior SW Engineer & Team Lead, Distributed AI Systems

About Us:

We are a fast-paced R&D team working on a cutting-edge project spanning multiple technical domains, including machine learning, computer vision, data pipelines, and software engineering.

Location: Ramat Gan

Role Overview:

Seeking a hands-on Software Engineer/Team Lead to architect, code, and lead the implementation of a distributed platform for complex AI/ML processing pipelines. The role involves technical leadership and customer interaction.

What You’ll Do

• Lead the engineering team building a distributed orchestration system.
• Own the design of core components and maintain the architectural coherence.
• Supervise implementation quality, review designs.
• Contribute significant production-level code: Python + systems-level components.
• Collaborate with early customers to define requirements, write clear technical documentation, and contribute to customer-facing materials.

Must-Have Skills

• Strong software engineering fundamentals: concurrency, multiprocessing, async I/O, queues, IPC, networking, memory-mapped files, linux system internals.
• Deep experience with distributed systems: controllers/workers, coordination, reliable communications, high availability, failover, state persistence, shared storage.
• Solid containerization experience: Docker image builds; environment isolation, dependencies management, multi-container pipelines structure.
• Familiarity with Kubernetes concepts: CRDs, operators/controllers, autoscaling, node-level behavior (not just “kubectl apply”).
• Hands-on experience with GPU workloads: CUDA/PyTorch/TensorRT, GPU scheduling, batching, profiling, performance bottleneck hunting.
• Excellent Python for production: robust structure, logging, testing, performance optimization; ability to work around GIL constraints.
• Ability to design for efficient inter-process communications and parallelism: multi-process, multi-thread, microbatching.
• Comfort debugging distributed failures: race conditions, container crashes etc.
• Proficiency in Git and related workflows: experience with Gitlab/Github, rebase-oriented strategies, and rapid concurrent development.
• Clear written and spoken English: technical documentation and conversations, knowledge of Hebrew and/or Russian – advantage.

• People with only front-end or web-service experience.
• ML researchers who don't write production systems.
• DevOps/infra engineers who don't write significant backend code.
• Candidates who only use Kubernetes for simple deployment.
• Junior engineers or those uncomfortable owning architecture + code.