Machine Learning Infrastructure Engineer

Orom AI is building a real-time spatial perception layer for Physical AI. Our models help robots, drones, and other camera-equipped systems understand where they are, what is around them, and how real-world environments are structured. We solve the most generalizable version of the perception problem as a baseline: monocular and edge-first, with no strict dependence on LiDAR, expensive sensor stacks, or cloud dependence. We fuse additional sensor inputs when a specific use case calls for it within the same methodology, improving robustness and accuracy depending on deployment fit.

We are building a truly novel perception stack by combining the power of compact neural networks with geometric grounding from classical optimization techniques. The neural modules learn strong 3D priors, while proven numerical solvers keep the results precise, stable, and interpretable. Our output is transformer-native and can be readily consumed as the context layer for downstream models including VLAs, VLMs and LLMs.

The foundational technology has been built and we are in the early stages of commercialization. Our monocular neural SLAM engine runs in real time on commodity hardware with either CPU or GPU and a small memory footprint, achieving strong localization accuracy on difficult sequences, substantially outperforming established classical and learned baselines. We have proved that our core technology is accurate and fast enough to help physical AI agents understand the world in production settings, and we are executing our first real-world deployments.

We are not building this alone. Orom AI was selected for the OpenAI Accelerator (one of 20 companies chosen from 280+ applicants across all fields of AI) and we are members of NVIDIA Inception and Microsoft for Startups. We hold letters of intent from organizations across logistics, construction, manufacturing, and autonomous drone navigation, and are executing our first partnerships with organizations across domains in the US and Europe.

We are building a world-class team to deliver on our mission by expanding our algorithmic research, increasing production readiness, and creating continual improvement loops using production data.

We care about high standards and strong execution. We make technical decisions based on truth-seeking rather than hierarchy or norms. We give each other true technical autonomy, trust each other, and correct mistakes together. Research is judged on its practical merits and always sits close to deployment. We are a close-knit team that moves quickly and enjoys solving hard technical problems together, while not taking ourselves too seriously.

You would work directly with a founding team that combines deep technical and commercial experience. Between us we bring a doctorate and seminal published research in machine learning and vision, hands-on experience building and globally deploying large-scale robotic systems in production, and a track record of founding, scaling, and exiting venture-backed companies. We have worked at the intersection of computer vision, robotics, and machine learning across leading technology companies and research institutions, and we are building Orom AI to apply that collective experience on one of the hardest open problems in Physical AI.

• Competitive salary

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• Meaningful equity. As one of our first hires you would receive founding team level ownership, with the upside that comes from building the core of the company from the start.
• High degree of ownership and impact: you'll own entire problem areas and make decisions that shape the technical core of the product and company foundations, with your work creating real-world deployments.
• Learning and development support; conference attendance
• We work primarily from our Cyprus office, on a three days a week hybrid basis, and we are open to flexibility for the right people.
• Relocation support for team members moving to Cyprus, which offers an expedited visa process, is a tax-beneficial and high quality of life environment, and is rapidly becoming a strong technology hub.
  

This role is about turning fast-moving ML research into reproducible, reliable, and shippable systems without slowing down experimentation. You will own the infrastructure that connects experimental models to production, working across training pipelines, data infrastructure, evaluation, deployment, monitoring, and internal tooling.

This role is ideal for someone who enjoys the full lifecycle of machine learning systems: making research work in messy production environments, data useful, deployments robust, and creating feedback loops from real-world deployments so models can continuously improve.

• Build and maintain training, evaluation, and deployment infrastructure for computer vision and spatial perception models.
• Develop systems for ingesting, organizing, validating, and learning from real-world video and sensor data.
• Create monitoring and diagnostics for model quality, runtime performance, data drift, and failure modes.
• Support efficient use of local and cloud compute for training, evaluation, and release workflows.
• Improve experiment tracking across research and engineering.

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• Work with co-founders and teammates to turn promising models into production-ready components.
• Build internal tools that make it easier for the team to inspect data, compare experiments, and understand regressions.
• Contribute to engineering standards around CI/CD, testing, reliability, and release quality.
  

• Strong Python skills and solid knowledge of software design
• Experience building production grade ML infrastructure, data pipelines, and model deployment workflows.
• Familiarity with PyTorch or similar ML frameworks.
• Experience deploying and optimizing models for edge and embedded targets, including runtime tooling such as CUDA and ONNX.
• Practical understanding of experiment tracking, dataset management, orchestration, CI/CD, and monitoring.
• Ability to work close to research while maintaining production discipline.
• Comfort with dynamic early-stage startup work, where responsibilities can evolve quickly.
• Bonus points for experience with computer vision, robotics, C++, distributed training, production data loops, or agentic infrastructure for orchestrating intelligent systems and workflows.