AI Engineer

A high-profile, venture-backed Deep Tech startup engineering next-generation accelerated compute platforms and custom silicon architectures for AI-centric hyperscale Data Centers.

Founded by an accomplished serial entrepreneur with deep semiconductor expertise and a proven exit track record, the company raised tens of millions of dollars in institutional funding within months alongside building a strong intellectual property portfolio.

The company provides a rare, early-stage opportunity to bridge state-of-the-art AI model architectures directly with novel hardware accelerators.

Located in the Central / Sharon District directly adjacent to primary train transit, operating on a hybrid model with one day working from home.

Role Description-

• Serving as an AI Inference & Performance Engineer / Applied AI Engineer, joining the core AI software group to bridge cutting-edge deep learning models with the company’s proprietary silicon architecture.
• Taking research-grade AI models and algorithmic concepts from Proof-of-Concept (PoC) into ultra-high-performance, production-scale execution deployed directly onto custom silicon.
• Leading comprehensive model optimization, quantization, pruning, and architectural adaptations to maximize throughput and minimize latency on target hardware.
• Driving deep-dive profiling, benchmarking, and bottleneck analysis across compute kernels, memory bandwidth, latency, and hardware utilization.
• Collaborating with compiler and runtime teams to optimize model execution graphs, operator fusions, and memory footprints within the inference runtime ecosystem.
• Designing and executing reproducible evaluation pipelines to measure model fidelity, throughput, and hardware efficiency at scale.

Requirements-

• 3 years of hands-on experience in roles such as AI Engineer, Applied AI / ML Engineer, AI Research Engineer, Inference Engineer, AI Performance Engineer, or equivalent – Mandatory
• B.Sc. in Computer Science, Software Engineering, Electrical Engineering, or related technical discipline from a recognized university OR equivalent hands-on technological experience from an elite IDF unit – Mandatory
• Deep theoretical and practical mastery of modern Deep Learning frameworks (e.g., PyTorch, TensorFlow, TensorRT, ONNX Runtime) – Mandatory
• Proven track record in AI model optimization, quantization, profiling, and inference acceleration – Mandatory
• Strong programming capabilities in Python and C++, with solid foundations in algorithmic complexity and performance optimization – Mandatory
• Hands-on understanding of compute bottlenecks, memory access patterns, and hardware-software co-design principles – Mandatory
• Proactive technical mindset, problem-solving independence, and fluent professional communication in English (written and verbal) – Mandatory