# Embodied AI / VLA Research Engineer at Maxinsights

Embodied AI / VLA Research Engineer at Maxinsights in Santa Clara. On-site, full-time. Posted October 7, 2026.

- Company: Maxinsights
- Location: Santa Clara
- Workplace: On-site
- Job type: Full-time
- Experience: 1+ year
- Posted: 2026-10-07
- Apply: https://jobs.ashbyhq.com/maxinsights/79cf8fc0-56fc-48ab-8732-f4d6eb53b0e8/application
- Source: https://jobmagic.ai/jobs/embodied-ai-vla-research-engineer-maxinsights-caxngm

## Requirements

- 1+ years of relevant industry or research experience in machine learning, robotics, computer vision, embodied AI, or a related field.
- Strong understanding of deep learning and modern machine learning methods.
- Experience with PyTorch or similar deep learning frameworks.
- Experience training and evaluating machine learning models.
- Strong programming skills in Python and familiarity with relevant ML/robotics tooling.
- Understanding of multimodal learning, computer vision, robotics, or related areas.
- Ability to work in a fast-paced startup environment and take ownership of technical problems from research through implementation.
- Strong problem-solving and debugging skills.

## Description

### Job Description:

**Position Overview**In this role, you will work on the **research, training, optimization, and real-world deployment of embodied AI models**, including Vision-Language-Action (VLA) models, World Models, and related robotics foundation models.
You will work across multimodal perception, robot control, long-horizon task planning, large-scale robot data, and model deployment. This is a highly hands-on role that involves both **model development and real-world robotic system integration**.

**Key Responsibilities**

### Embodied AI Model Development

- Research and develop **Vision-Language-Action (VLA), World Model, and other embodied AI foundation models**.

- Work on areas including:

- Robotic manipulation

- Multimodal perception and control

- Long-horizon task planning

- Vision-language-action reasoning

- Robot-environment interaction

- Develop and optimize models for real-world robotic applications.

- Translate research ideas into practical models and systems that can operate reliably on physical robots.

### **Foundation Model Training & Optimization**

- Train and optimize embodied AI foundation models using **large-scale real-robot datasets and egocentric human demonstration data**.

- Design approaches for incorporating multimodal inputs such as:

- Vision

- Force

- Tactile sensing

- Proprioception

- Other robot and environmental signals

- Develop and evaluate multimodal fusion architectures.

- Conduct model training, evaluation, benchmarking, and performance optimization.

- Analyze model performance and identify opportunities to improve training efficiency, generalization, and real-world performance.

### Robotics Data Pipeline

- Build and improve large-scale robotics data pipelines for model training.

- Develop processes for:

- Data cleaning

- Data filtering

- Resampling

- Data augmentation

- Data quality evaluation

- Design scalable data pipelines capable of supporting large volumes of robot and human demonstration data.

- Work closely with data and robotics teams to improve dataset quality and training efficiency.

### Model Deployment & Real-Robot Testing

- Deploy trained models to physical robot systems.

- Perform **real-world robot debugging, testing, and performance optimization**.

- Diagnose issues across models, sensors, software, and robotic hardware.

- Iterate between model training and real-world testing to improve system performance.

- Help ensure models operate reliably and consistently in real-world environments.

 **Qualifications**

### Required

- **1+ years of relevant industry or research experience** in machine learning, robotics, computer vision, embodied AI, or a related field.

- Strong understanding of deep learning and modern machine learning methods.

- Experience with **PyTorch** or similar deep learning frameworks.

- Experience training and evaluating machine learning models.

- Strong programming skills in **Python** and familiarity with relevant ML/robotics tooling.

- Understanding of multimodal learning, computer vision, robotics, or related areas.

- Ability to work in a fast-paced startup environment and take ownership of technical problems from research through implementation.

- Strong problem-solving and debugging skills.

### Strong Plus

**Real-World Robotics Deployment**

- Experience deploying and debugging machine learning models on **physical robot systems**.

- Ability to bring models from development into real-world robotic environments.

- Experience troubleshooting and stabilizing robotic systems in production or experimental environments.

**Multimodal / VLA Models**

- Experience working with **force, tactile, or other multimodal sensing**.

- Experience designing or training multimodal fusion models.

- Hands-on experience with **VLA models**, including model design, training, evaluation, or real-world applications.

- Experience with robotic manipulation or embodied AI systems.

**Large-Scale Distributed Training**

- Experience with large-scale distributed model training.

- Familiarity with **DDP, DeepSpeed, FSDP**, or similar distributed training frameworks.

- Experience optimizing training performance, GPU utilization, memory usage, or training throughput.

- Experience working with large-scale datasets and distributed data pipelines.

 **Ideal Candidate**We are looking for an engineer who is excited about the intersection of **foundation models and physical robotics**.
The ideal candidate is:

- Hands-on and comfortable moving between **research, coding, experimentation, and real-world robot testing**.

- Interested in solving problems that cannot be addressed through simulation or software alone.

- Comfortable working with large-scale datasets and modern foundation-model architectures.

- Able to take ownership of a problem from **data → training → evaluation → deployment → real-world iteration**.

- Comfortable working in an early-stage environment where priorities can move quickly.

- Curious about emerging VLA, World Model, and embodied AI research and able to translate new ideas into working systems.

 

### **Why Join MaxInsights?**

- Work directly on **embodied AI and robotics foundation models**.

- Work with large-scale real-world robotics and human demonstration data.

- Gain hands-on experience across the full AI development lifecycle, from data pipelines to real-robot deployment.

- Work in a fast-moving startup environment with significant ownership and technical autonomy.

- Collaborate with teams working at the forefront of robotics and foundation-model development.

Default Benefits:
- Health insurance

- Vision care

- Dental coverage

- 401(k)

- Paid holidays

- PTO (Paid Time Off)

- Sick leave