Applied Research Scientist / Engineer - Deployment

At Rhoda AI, we're building the full-stack foundation for the next generation of humanoid robots — from high-performance, software-defined hardware to the foundational models and video world models that control it. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling scenarios unseen in training. We work at the intersection of large-scale learning, robotics, and systems, with a research team that includes researchers from Stanford, Berkeley, Harvard, and beyond. We're not building a feature; we're building a new computing platform for physical work — and with over $400M raised, we're investing aggressively in the R&D, hardware development, and manufacturing scale-up to make that 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