---
title: 'Software Engineer Intern, Inference at Rhoda AI'
canonical: 'https://feeny.ai/job/software-engineer-intern-inference-rhoda-ai-mountain-view-0yz1xmmcxnsq'
type: 'job'
last_seen: '2026-10-21'
---

# Software Engineer Intern, Inference at Rhoda AI

- **Company:** Rhoda AI
- **Location:** Mountain View, CA
- **Employment:** internship
- **Posted:** 2026-10-21
- **Last confirmed live:** 2026-10-21
- **Apply:** https://jobs.ashbyhq.com/rhoda-ai/2de33083-782f-489e-9b91-f40761a36bf4

## 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.

## The Role

Our foundation world models have to run fast enough to control robots in the real world, and efficiently enough to support large-scale research. As an Inference Infrastructure Engineer Intern, you'll work with our infra and research engineers to make model inference faster and more efficient across the stack, from data pipelines down to GPU kernels. You'll own a scoped, high-impact project end to end, ship it into our production or research workflow, and present your results to the team.

## What You'll Do

- Profile and benchmark inference for large video and world models to find bottlenecks in latency, throughput, and memory.
- Apply and evaluate inference optimization techniques like quantization and low-precision inference, step distillation and sampling acceleration for diffusion-style models, attention and caching optimizations across frames, model compilation, and graph-level optimizations.
- Make data movement and processing more efficient: video decoding and preprocessing, input pipelines, host–device transfer, caching, and storage/IO paths that feed inference and training.
- Write and tune GPU kernels (CUDA, Triton, or similar) for performance-critical operators, and integrate them into our inference stack.
- Build tooling and benchmarks that let the team track performance regressions and improvements reliably.
- Work with researchers to turn promising ideas into production-quality infrastructure.

## What We're Looking For

You don't need all of these. Strong depth in any one of the areas below is enough to apply:

- ML inference optimization: hands-on experience making deep learning models run faster or cheaper, e.g. quantization (INT8/FP8), pruning/distillation, operator fusion, mixed precision, or compilers and runtimes such as torch.compile, TensorRT, ONNX Runtime, or TVM.
- Data efficiency optimization: experience speeding up data loading and preprocessing pipelines (especially video or other high-bandwidth sensor data), cutting IO and memory overhead, or improving data/compute utilization in ML systems.
- GPU kernel optimization: experience writing or tuning GPU kernels in CUDA, Triton, CUTLASS, or similar; familiarity with the GPU memory hierarchy, occupancy, and profiling tools (Nsight Systems/Compute, PyTorch profiler).

## Basic Qualifications

- Currently pursuing a BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, or a related field.
- Strong programming skills in Python and/or C++.
- Solid understanding of deep learning fundamentals and at least one framework (PyTorch preferred).
- Comfortable reading performance data and reasoning about trade-offs between latency, throughput, memory, and accuracy.

## Nice to Have

- Experience with video model inference is a huge plus, e.g. optimizing video generation, video diffusion, or world models, or real-time video understanding pipelines.
- Research experience in systems or ML, e.g. publications or substantial research projects (MLSys, OSDI/SOSP, CVPR/ICCV, NeurIPS/ICML/ICLR, or similar).
- Experience with real-time or latency-critical inference, e.g. on robots or edge/embedded accelerators.
- Experience with multi-GPU inference (tensor/sequence/pipeline parallelism).
- Contributions to open-source ML systems projects.

## What You'll Get

- A real project with measurable impact on robots operating in the real world.
- Mentorship from experienced infra and research engineers.
- Access to modern GPU compute for experiments.

## 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

- [Software Engineer, Rhoda Platform](https://feeny.ai/job/software-engineer-rhoda-platform-rhoda-ai-mountain-view-118z5qm9f5q0) — Mountain View, CA
- [Software Engineer Intern, Rhoda Platform](https://feeny.ai/job/software-engineer-intern-rhoda-platform-rhoda-ai-mountain-view-3s3s0rkqmbkw) — Mountain View, CA
- [Technical Program Manager](https://feeny.ai/job/technical-program-manager-rhoda-ai-mountain-view-1vmebb3sybwj) — Mountain View, CA
- [Solution Engineer](https://feeny.ai/job/solution-engineer-rhoda-ai-mountain-view-1fkt9bqkr8g7) — Mountain View, CA
- [Head of Talent](https://feeny.ai/job/head-of-talent-rhoda-ai-mountain-view-pt49x7f3hg0b) — Mountain View, CA
- [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
- [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
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