# Senior / Staff AI Research Scientist, Foundation Models at RoboForce

Senior / Staff AI Research Scientist, Foundation Models at RoboForce in Milpitas, CA. On-site, full-time, paying $200k–$360k. Posted November 10, 2025.

- Company: RoboForce
- Location: Milpitas, CA
- Workplace: On-site
- Job type: Full-time
- Pay: $200k–$360k
- Experience: 4+ years
- Posted: 2025-11-10
- Apply: https://job-boards.greenhouse.io/roboforce/jobs/4981345008
- Source: https://jobmagic.ai/jobs/senior-staff-ai-research-scientist-foundation-models-roboforce-jfadnq

## Requirements

- PhD degree in Machine Learning, Robotics, or related field, or Master's degree with 4+ years of relevant experience.
- Proficiency in Python and deep learning frameworks (e.g., PyTorch, JAX).
- Expertise in large foundation models (VLM, VLA, etc.).
- Strong understanding of world model architectures and action-conditioned generative modeling for robot learning.
- Decent understanding of multimodal models, modern ML architectures (transformers, diffusion models, etc.).
- Requires 5 days/week in-office collaboration with the teams.

## Description

**Why RoboForce**

RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. The company's robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability.
 
We are looking for a **Senior / Staff AI Research Scientist, Foundation Models** to advance robotic embodied intelligence. In this role, you will develop algorithms that enable robots to understand their environment, interpret and execute tasks, and communicate seamlessly with humans — with a particular focus on building and training world models that allow robots to predict, plan, and generalize across complex physical tasks.
 
**Responsibilities**

- Design and deploy vision-language(-action) models (VLM/VLA) for contextual understanding and generalized robot action policies.

- Develop and train world models for action-conditioned prediction, long-horizon planning, and environment simulation — enabling robots to reason about the consequences of their actions before execution.

- Research approaches to improve world model fidelity using multi-modal inputs including vision, language, proprioception, and spatial representations.

- Develop foundation models with spatial reasoning capabilities to achieve high-precision robotic actions.

- Integrate multi-modal data sources (vision, language, speech, etc.) to enable natural human-robot communication.

- Optimize and deploy models as production-grade solutions on RoboForce robotic platforms.

**Requirements**

- PhD degree in Machine Learning, Robotics, or related field, or Master's degree with 4+ years of relevant experience.

- Proficiency in Python and deep learning frameworks (e.g., PyTorch, JAX).

- Expertise in large foundation models (VLM, VLA, etc.).

- Strong understanding of world model architectures and action-conditioned generative modeling for robot learning.

- Decent understanding of multimodal models, modern ML architectures (transformers, diffusion models, etc.).

- **Requires 5 days/week in-office collaboration with the teams.Bonus Qualifications**

- Experience with video generation or prediction models (e.g., diffusion-based video models, autoregressive video transformers) and their application to world modeling or synthetic data generation for robot learning.

- Strong publication record at top conferences (NeurIPS, ICML, CVPR, ICCV, CoRL, ICRA, or equivalent).

- Expertise in neural network deployment (e.g., TensorRT) and GPU programming with CUDA.

- Proven ability to design scalable experimentation and data pipelines.

**Benefits**

- Competitive stock options/equity programs.

- Health, dental, and vision insurance, 401(k) plan.

- Visa sponsorship and green card support for qualified candidates.

- Lunches and dinners, a fully stocked kitchen, and regular team-building events.

 

Compensation: Salary $200,000–$360,000 USD + Bonus + Equity
 
The base salary range above represents the expected compensation for this full-time U.S. position. Final compensation will be determined based on role scope, level, location, job-related skills, experience, and relevant education or training, and may fall outside the listed range in exceptional cases.