Research Member of Technical Staff- Applied Engineering

About the company

Rhoda AI develops video-predictive control foundation models that enable robots to autonomously adapt to real-world manufacturing and logistics environments.

About this role

At Rhoda AI, we’re building the next generation of generalist intelligent robots. We own the full robotics stack from high-performance hardware and robot systems to the infrastructure and state-of-the-art foundation world models that control our robots. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling long-tail edge cases, made possible by our cutting edge research and end-to-end system design. We've raised over $450M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale-up to make generalist robotics a reality. We're looking for Applied Research Scientists and Research Engineers to take our foundation world models and adapt them for specific customer applications and industry use cases. We hire across levels — from senior/MTS to staff. This is a customer-facing role at the intersection of research and deployment — you'll work directly with partners and end users to understand their needs, translate them into model adaptations, and deliver measurable improvements in real-world settings across industries like logistics, manufacturing, and beyond. What You'll Do - Work directly with customers and partners to understand application requirements and translate them into concrete model adaptation strategies - Fine-tune and adapt our foundation world models for domain-specific tasks, environments, and operational constraints - Design and run targeted experiments to evaluate model performance against customer-defined success criteria - Build application-specific evaluation benchmarks and testing frameworks to validate model behavior in real customer environments - Identify gaps between general-purpose model capabilities and the requirements of specific use cases, and drive research to close them - Collaborate with the core research team to surface patterns and insights from customer deployments that inform foundational model development - Communicate technical findings clearly to both technical and non-technical stakeholders What We're Looking For - Strong ML research and engineering skills with hands-on experience fine-tuning or adapting large models - Ability to move fluidly between customer requirements and technical implementation - Solid understanding of modern ML pipelines: pre-training, fine-tuning, evaluation, and deployment - Comfort working across teams — research, engineering, and customer-facing functions - Strong communication skills: ability to explain model behavior and tradeoffs to non-technical audiences - Experience in a customer-facing, applied research, or solutions engineering role - Staff-level candidates are expected to define technical direction and drive research strategy independently; senior/MTS candidates execute complex projects with strong fundamentals and growing scope Nice to Have (But Not Required) - Experience adapting foundation models (LLMs, VLMs, or policy models) to domain-specific applications - Familiarity with one or more relevant verticals (e.g., logistics, manufacturing, warehouse automation, agriculture) - Familiarity with inference optimization and runtime constraints (latency, memory, hardware targets) — sufficient to work alongside inference engineers, not own it - Experience with sim-to-real transfer or adapting models trained in one environment to operate in another - Hands-on experience with real robot deployments in production or near-production settings - PhD or strong research background in ML, Robotics, or a related field Why This Role - Rare combination of research depth and direct customer impact — you see your work matter in the real world - Surface insights from real-world deployments that feed back into foundational model development - Work across industries and applications with significant variety in problems and environments - High visibility within the company as the bridge between our core models and the customers who use them