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About the job

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