Applied Research Scientist / Engineer - Deployment

Rhoda Ai

Palo Alto, US

On-site

Who this role is best for

Best suited to mid-level ML researchers comfortable with customer-facing roles and adapting foundation models for real-world applications.

Best fit for

ML researchers who thrive in bridging customer needs with technical solutions.
— “Work directly with customers and partners to understand application requirements”
Engineers with hands-on experience fine-tuning large models for specific domains.
— “Strong ML research and engineering skills with hands-on experience fine-tuning or adapting large models”
Professionals comfortable explaining technical trade-offs to non-technical stakeholders.
— “ability to explain model behavior and tradeoffs to non-technical audiences”

Things to consider

Role requires balancing research depth with direct customer interactions.
— “customer-facing role at the intersection of research and deployment”
Expect to work across multiple industries and applications.
— “Work across industries and applications with significant variety in problems and environments”

How to stand out

Highlight specific examples of adapting models to domain-specific tasks.
— “Fine-tune and adapt our foundation world models for domain-specific tasks”
Demonstrate experience with real-world model deployments in production.
— “Hands-on experience with real robot deployments in production or near-production settings”
Showcase communication skills in translating technical findings to stakeholders.
— “Communicate technical findings clearly to both technical and non-technical stakeholders”

Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · TeamLevel · Senior

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

model adaptation for customer needs
performance evaluation
research-driven improvements

Typical background

ML researchrobotics

Skills & requirements

Required

ML Research And EngineeringModel Fine-tuningDomain-specific Model AdaptationCustomer-facing ResearchTechnical Communication

Preferred

Foundation Model AdaptationSim-to-real TransferReal Robot Deployments

Stack & domain

Ml ResearchEngineeringModel AdaptationExperiment DesignEvaluation BenchmarksModel PerformanceCustomer RequirementsTechnical ImplementationMl PipelinesFoundation World ModelsRoboticsHardware DevelopmentManufacturing Scale-upCommunicationTeamworkProblem-solvingCustomer Relationship ManagementStrategic PlanningExecutionTrust-buildingGeneralist Intelligent RobotsReal-world EnvironmentsLong-tail Edge CasesLogisticsManufacturingWarehouse AutomationAgriculture

About the role

Original posting from Rhoda Ai via Ashby

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 $400M 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

Source: Rhoda Ai careers (Ashby)