---
title: 'Research Member of Technical Staff- Efficient Modeling at Rhoda AI'
canonical: 'https://feeny.ai/job/research-member-of-technical-staff-efficient-modeling-rhoda-ai-mountain-view-kpj2pptrb53a'
type: 'job'
last_seen: '2026-10-14'
---

# Research Member of Technical Staff- Efficient Modeling at Rhoda AI

- **Company:** Rhoda AI
- **Location:** Mountain View, CA
- **Employment:** full-time
- **Posted:** 2026-05-18
- **Last confirmed live:** 2026-10-14
- **Apply:** https://jobs.ashbyhq.com/rhoda-ai/5445a936-b503-4cf3-aecf-7e4dca3ed7cf/application

**Skills:** PyTorch, Model Compression, Quantization, Distillation, Pruning, CUDA, TensorRT, Transformer Architecture, Hardware-Aware Optimization, Efficient Video Model Architectures, Multimodal Model Architectures, Edge Deployment, Jetson, Custom ASICs, Speculative Decoding, Early Exit, Adaptive Compute, Physical Robot Deployment

> Research and implement model compression techniques to make foundation world models faster, smaller, and more deployable for real-time robot operation. Design efficient architectures and attention mechanisms, develop training strategies, and profile models across hardware targets to bridge the gap between research-s...

## 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 a Research Scientist or Research Engineer focused on model efficiency — making our foundation world models faster, smaller, and more deployable without sacrificing capability. This work is critical to closing the gap between research-scale models and real-time operation on robot hardware.

## What You'll Do

- Research and implement model compression techniques: quantization, pruning, structured sparsity, distillation, and low-rank approximation
- Design efficient architectures and attention mechanisms suited to real-time inference on edge and robot hardware
- Develop training strategies that produce better accuracy-efficiency tradeoffs from the start
- Profile and benchmark models across hardware targets to identify and resolve efficiency bottlenecks
- Build evaluation frameworks that measure capability retention after compression or architecture changes
- Collaborate with training systems and deployment teams to ensure efficient models translate to faster real-world inference
- Publish and present work at top-tier venues

## What We're Looking For

- Strong understanding of model compression and efficient architectures for large models
- Hands-on experience with quantization, distillation, or pruning applied to transformers or large neural networks
- Deep knowledge of where efficiency gains are possible in modern architectures
- Proficiency with PyTorch and familiarity with hardware-aware optimization (CUDA, TensorRT, or similar)
- Ability to run principled experiments that characterize capability-efficiency tradeoffs

## Nice to Have (But Not Required)

- PhD in ML, CS, or a related field — or equivalent research/engineering experience
- Publication record at NeurIPS, ICML, ICLR, MLSys, or related venues
- Experience with efficient video or multimodal model architectures
- Familiarity with edge deployment targets (Jetson, custom ASICs, or mobile hardware)
- Prior work on speculative decoding, early exit, or adaptive compute
- Experience deploying compressed models on physical robots or latency-constrained systems

## Why This Role

- Bridge the gap between large-scale research models and real-time robot deployments
- Your work determines whether frontier capabilities actually run on our hardware
- High leverage: efficiency improvements benefit every model the team trains and deploys
- Work at a rare intersection of deep learning research and systems

## About Rhoda AI

## Company Overview
- **One-liner**: Rhoda AI builds robot foundation models that learn from internet-scale video to enable manipulation-capable robots to generalize in real-world industrial environments.
- **Entity Type**: Private (Seed + Series A; $680M total funding)
- **Headquarters**: Palo Alto, CA, United States
- **Founded**: 2024
- **Founders**: Jagdeep Singh (CEO), Eric Chan (Chief Scientist), Changan Chen (Chief Research Officer), Andrew Wooten, Gordon Wetzstein (Scientific Advisor) [rhoda.ai/team](https://www.rhoda.ai/team)

## Core Business
- **Primary industry**: Robotics, Artificial Intelligence, Hardware
- **Target customers**: B2B – industrial enterprises in automotive, manufacturing, logistics, and e-commerce
- **Mission/purpose**: “Building at the frontier of embodied intelligence” and “Designing intelligence for the real world” – deploying general-purpose robot foundation models that adapt to the variability of commercial and industrial environments. [rhoda.ai](https://www.rhoda.ai/) | [linkedin.com](https://www.linkedin.com/company/rhoda-ai)

## Products & Services
- **Rhoda Robot Platform (Hardware + Software)**: A turnkey robotic system with custom actuators (25kg rated, 40kg peak payload), safety-rated vision, wheel-base, and brakes in every actuator. The platform runs the **Direct Video Action** model, which pre-trains on over a million real-world videos and post-trains on task-specific robot data. Long-context windows enable single-shot learning from human demonstrations without retraining. [rhoda.ai](https://www.rhoda.ai/)
- **FutureVision Intelligence Layer**: A proprietary architecture that handles real-world industrial tasks autonomously by generalizing beyond conventional VLA pipelines. [rhoda.ai](https://www.rhoda.ai/)
- **Deployment Services**: End-to-end solutions for tasks such as returns processing, bearing decanting, container breakdown, and material transport in logistics and e-commerce facilities. [rhoda.ai](https://www.rhoda.ai/)

## Market Standing
- **Valuation**: Not publicly disclosed
- **Key Metric**: Total funding **$680M** – Seed round $67.4M (April 2024), Series A $162.6M (April 2025), and Series A $450M (March 2026) with 4 investors. [linkedin.com](https://www.linkedin.com/company/rhoda-ai)
- **Notable Investors/Partners**: Not individually named in available sources; partners include customers in automotive, manufacturing, logistics, and e-commerce. [rhoda.ai](https://www.rhoda.ai/)
- **Growth Signals**: Headcount growth of **+20% monthly** (LinkedIn reports 55 employees; Built In reports 73 employees – conflicting data). Talent sourced from companies like QuantumScape, Flexiv Robotics, Scale AI, Google, Meta, NVIDIA, and Stanford University. [linkedin.com](https://www.linkedin.com/company/rhoda-ai) | [builtin.com](https://builtin.com/company/rhoda-ai)

## Competitive Advantages
- **Direct Video Action architecture** with web-scale video pre-training gives a strong prior on motion, physics, and dynamics, enabling generalization beyond lab settings.
- **Custom high-performance actuators** (25kg rated, 40kg peak) with safety-rated brakes and vision – designed for 3+ years of continuous operation at rated payload.
- **Long-context in-context learning** allows the robot to perform tasks single-shot from human demonstrations without retraining.
- **Turnkey industrial deployments** that handle ambiguity and variability (e.g., debris, transparent bags, heavy boxes) – tasks customers initially believed could not be automated. [rhoda.ai](https://www.rhoda.ai/)

## Strategic Focus
- Scaling robot intelligence through a pre-training/post-training paradigm, moving from controlled labs to reliable production environments.
- Expanding commercial deployments across automotive, manufacturing, logistics, and e-commerce verticals.
- Pursuing a path to **physical AGI** by combining foundation models with custom hardware for real-world adaptability. [rhoda.ai](https://www.rhoda.ai/)

## Why Work Here
- **Culture**: “Building at the frontier of embodied intelligence” – emphasis on hard problems, first-principles thinking, and category-defining systems. [linkedin.com](https://www.linkedin.com/company/rhoda-ai)
- **Work policy**: Hybrid workspace (employees combine remote and on-site work), with offices in Mountain View and Palo Alto, CA. [builtin.com](https://builtin.com/company/rhoda-ai)
- **Engineering culture**: Strong research focus (Chief Research Officer, Chief Scientist, many research engineers/scientists). Recent job postings include Research Scientist, Robot Perception Engineer, Mechanical Engineer, Fullstack Engineer, and Product Manager. [builtin.com](https://builtin.com/company/rhoda-ai)
- **Notable perks**: Not explicitly listed, but the company is well-funded ($680M) and at an early stage (founded 2024), offering significant growth opportunity and impact.

## Sources
1. [rhoda.ai](https://www.rhoda.ai/) – Company website (product, technology, commercial applications)
2. [rhoda.ai/careers](https://www.rhoda.ai/careers) – Careers page
3. [rhoda.ai/team](https://www.rhoda.ai/team) – Leadership team and co-founders
4. [builtin.com](https://builtin.com/company/rhoda-ai) – Built In profile (employee count, offices, job listings, hybrid policy)
5. [linkedin.com](https://www.linkedin.com/company/rhoda-ai) – LinkedIn company page (funding, headcount, executive list, talent sources)

## Other roles at Rhoda AI

- [Staff Product Manager- Robot & Fleet Interfaces](https://feeny.ai/job/staff-product-manager-robot-fleet-interfaces-rhoda-ai-mountain-view-sw5va827cxvb) — Mountain View, CA
- [Associate Engineering Product Manager- Robot Data Systems](https://feeny.ai/job/associate-engineering-product-manager-robot-data-systems-rhoda-ai-mountain-view-p6pw133hymk0) — Mountain View, CA
- [Robot Software Engineer Internship](https://feeny.ai/job/robot-software-engineer-internship-rhoda-ai-mountain-view-3s3s0rkqmbkw) — Mountain View, CA
- [Social Media & Content Lead- AI Research](https://feeny.ai/job/social-media-content-lead-ai-research-rhoda-ai-mountain-view-09kzs6nda5w7) — Mountain View, CA
- [Research Member of Technical Staff- Training Systems](https://feeny.ai/job/research-member-of-technical-staff-training-systems-rhoda-ai-mountain-view-n7d7bvemaev0) — Mountain View, CA
- [Field Robotics Deployment Lead](https://feeny.ai/job/field-robotics-deployment-lead-rhoda-ai-mountain-view-v91gfb5byq4a) — Mountain View, CA
- [Senior UX/UI Designer](https://feeny.ai/job/senior-ux-ui-designer-rhoda-ai-mountain-view-tb6zs4gkw722) — Mountain View, CA
- [Staff Thermal Systems Integration Engineer](https://feeny.ai/job/staff-thermal-systems-integration-engineer-rhoda-ai-mountain-view-h0adk89xs839) — Mountain View, CA
- [Robotics Test Engineer](https://feeny.ai/job/robotics-test-engineer-rhoda-ai-mountain-view-fawz7jcaj9pv) — Mountain View, CA
- [Senior Hardware Reliability Engineer](https://feeny.ai/job/senior-hardware-reliability-engineer-rhoda-ai-mountain-view-tb5a3r2wpf2a) — Mountain View, CA