Job description

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

Requirements

Must-haves

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
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
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

Nice-to-haves

Nice to Have (But Not Required)

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiml

Platform: ashby
Effort: quick