AI Researcher

About Us At InOrbit.AI, we are defining the management layer for physical AI through cutting-edge robot orchestration. Our mission is to unlock the full potential of complex robot fleets, bridging high-level intelligence with real-world physical execution. We cultivate an innovation-driven culture where team members drive core breakthroughs in agentic AI and physical AI, pioneering how multi-agent robotic systems perceive, reason, and interact in dynamic environments. The Opportunity We are seeking a highly specialized and technically versatile AI Researcher to operate as a high-impact team member. This unique role combines deep AI research with robust production-grade ML engineering and full-stack system development. You will be driving critical AI features, responsible for the end-to-end lifecycle—from foundational algorithm research and data pipeline construction to deploying scalable, user-facing applications that utilize the models. This role is best suited for an expert who can seamlessly transition between prototyping new AI models, architecting MLOps infrastructure, and developing APIs and interfaces. Responsibilities: Foundational Research & Novel Prototyping: Lead advanced, hands‑on AI research to explore novel algorithms, generate intellectual property, and rapidly prototype experimental models that directly inform and unlock new product capabilities. End-to-End System Architecture: Contribute to the design and implementation of the entire AI ecosystem, including the core ML model, data pipelines (MLOps), application APIs, and integration layers necessary to ship features. Full Stack Development & Integration: Develop and maintain the end-to-end software components required to integrate AI models into the InOrbit platform, including backend services, robust APIs, and user-facing features (where applicable). Production Model Deployment & MLOps: Independently manage the full AI lifecycle, ensuring high-performance, cost-effective, and secure deployment of models into production using best-in-class MLOps practices. Technical Authority & Standard Setting: Act as a technical authority in the AI domain, setting code quality, architectural standards, and operational excellence. Performance & Optimization: Lead efforts to identify and resolve performance bottlenecks, from model inference latency to API response times and data throughput. Cross-functional Collaboration: Partner with Product Management and core Engineering to translate complex technical concepts and research breakthroughs into shippable, business-driving features. Qualifications: Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a closely related technical field. Candidates who left a PhD program to found or join a startup are encouraged to apply. Minimum of 5 years of progressive, hands‑on technical experience in a combination of AI Research, Machine Learning Engineering, and Full Stack/Systems Development. Proven track record of successfully leading/contributing to the technical architecture and delivery of complex, large-scale software systems driven by novel AI/ML models. Deep, hands‑on expertise in advanced AI/ML techniques (e.g., reinforcement learning, agentic AI, deep learning) and expert-level proficiency in at least one modern full-stack programming language/framework. Strong understanding and practical experience with MLOps, cloud-based infrastructure (e.g., GCP, AWS, Azure), and data pipeline construction. Ability to solve ambiguous technical problems across the stack and drive major technical initiatives independently. Strong communication skills, with the ability to articulate architectural decisions and research findings to both technical and non-technical audiences. Preferred Qualifications: Extensive use of AI coding and design agents, strong spec-writing skills. Experience building and maintaining real-time systems and high-performance, low-latency APIs in a production environment. Contribution to open-source projects or peer-reviewed research in AI/ML. Experience in robotics, autonomous systems, or other forms of physical AI. Compensation: We offer a competitive and flexible compensation package, considering a wide range of experience levels and geographic locations. The expected compensation for this role includes base salary, a performance-based bonus, benefits and equity. Your actual compensation will be determined by your skills, experience, and alignment with the scope of the role. Candidates must possess current and valid work authorization for the United States. InOrbit.AI does not provide sponsorship for employment visas (including H-1B, OPT/CPT, TN, etc.) for this position, now or in the future. #J-18808-Ljbffr InOrbit.ai