DevOps Engineer
Infrastructure
We are looking for a DevOps Engineer to own and scale the infrastructure behind our AI systems.
Ramat Gan · Full-time · Hybrid
Deep Learning Engineer
We are looking for a Deep Learning Engineer to help design, implement, optimize, and scale the training and inference systems behind our models.
Engineering · Ramat Gan · Full-time · Hybrid
Apply for this role
01
About the role
doubleAI is building next-generation reasoning and code-optimization agents that push the frontier of artificial expert intelligence. We are looking for a Deep Learning Engineer to help design, implement, optimize, and scale the training and inference systems behind our models.
This is not a pure research role. We are looking for an exceptional engineer who can work close to the model stack, understand deep learning systems end-to-end, and turn ambitious ideas into reliable, high-performance infrastructure. You will work across large-scale training, distributed systems, inference optimization, model evaluation, and deployment — helping us build AI systems that are scalable, efficient, and robust.
02
What you'll do
Collaborate with cross-functional teams to bring research breakthroughs into production environments.
Build tools, abstractions, and infrastructure that make model training, evaluation, and deployment faster and more reliable.
Analyze and overcome performance and scalability challenges in large-scale distributed training.
Debug complex issues across the full ML stack: data loading, model code, distributed execution, GPU utilization, memory usage, networking, checkpointing, and serving.
Work with modern training and inference frameworks such as Megatron, TorchTitan, PyTorch, and vLLM.
03
What we're looking for
- MSc or PhD in AI, Machine Learning or Algorithms.
- Strong software engineering skills, with experience designing and building complex systems.
- Practical understanding of distributed training concepts: data parallelism, tensor parallelism, pipeline parallelism, sharding, checkpointing, synchronization, and fault tolerance.
- Experience working with inference systems and serving frameworks, ideally including vLLM or similar high-throughput LLM serving infrastructure.
- Solid understanding of machine learning fundamentals, including optimization, transformers, attention mechanisms, numerical stability, and model evaluation.
- Low-level systems intuition: performance profiling, memory layout, GPU utilization, networking bottlenecks, and tradeoffs between latency, throughput, and reliability.
- Ability to write clean, maintainable, well-tested code in fast-moving environments.
- Strong ownership mindset, good judgment, and the ability to operate with high agency under uncertainty.
Nice to have
- Experience training or serving large language models at scale.
- Experience with Mixture-of-Experts, model parallelism, or large distributed GPU clusters.
- Familiarity with CUDA kernels, Triton, NCCL, RDMA, Kubernetes, Slurm, or cloud/HPC infrastructure.
- Experience optimizing inference systems for throughput, latency, batching, KV-cache efficiency, or cost.
- Experience building internal ML platforms, experiment infrastructure, observability tools, or model evaluation pipelines.
- Background in reinforcement learning, post-training, synthetic data, or agentic systems.
- Contributions to open-source ML systems or deep learning infrastructure.
- Strong publication record or demonstrable research contributions.
Interested in this role?
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