Machine Learning Engineer

A high-growth Tech innovator redefining the boundaries of electronic warfare and autonomous threat mitigation.

Backed by significant Tier-1 venture capital and led by a veteran executive team, the company provides sophisticated, multidisciplinary solutions to government and military clients worldwide.

Their core platform leverages proprietary RF Cyber-Takeover technology to detect and neutralize hostile aerial threats while ensuring operational safety.

Operating from a central technology hub with a hybrid work model, the organization fosters an elite, mission-critical environment for engineers seeking to solve high-stakes physical-layer challenges.

Responsibilities-

• process diverse inputs from Signal Processing (RF), Sensor Data, and Computer Vision (CV).
• Real-Time Deployment: Implementing high-performance models for edge-computing environments where latency and accuracy are mission-critical in the field.
• Agentic Innovation: Developing Agentic Workflows and RAG systems to streamline R&D processes and enhance the company’s internal research and development capabilities.
• Deep Learning R&D: Leveraging advanced frameworks to develop complex architectures for pattern recognition, threat classification, and multimodal data fusion.
• Autonomous Systems Evolution: Architecting robust ML pipelines to support continuous training and evaluation at scale for autonomous defense systems.

Requirements-

• Academic Foundation: B.Sc. in Computer Science or a related scientific field from a leading university – Mandatory.
• ML Seniority: Significant professional experience in Machine Learning, with a proven track record of taking models from early-stage research to production – Mandatory.
• Deep Learning Expertise: Extensive hands-on experience in developing and training complex Deep Learning models – Mandatory.
• Framework Mastery: High proficiency with modern ML frameworks (e.g., PyTorch or TensorFlow) – Mandatory.
• Advanced AI: Experience with AI Agents, RAG systems, or Agentic Workflows – Significant Advantage.
• Domain Expertise: Practical experience in Signal Processing, RF, Computer Vision, or Multimodal data – Significant Advantage.
• Problem-Solving Mindset: The ability to tackle complex "Real-world" problems where sophisticated software meets physical-layer hardware in dynamic environments.