As an ML Engineer at FieldAI, you will help build the next-generation Field Foundation Model (FFM), powering a global fleet of autonomous robots deployed across diverse environments. Your contributions will directly shape how we scale – through advances in model architecture, training methodologies, and deployment strategies.
You’ll collaborate closely with machine learning scientists, software engineers, and robotics experts to design and implement FFM capabilities that generalize across tasks and environments. Beyond model development, you’ll also support deployment and monitoring to ensure smooth integration and reliable real-world performance.
This role offers the opportunity to work with cutting-edge technologies, solve complex challenges, and directly impact large-scale robot deployments.
What You’ll Get To Do
- Design, train, and deploy state-of-the-art machine learning models for end-to-end learning based navigation stack.
- Work with deep learning architectures such as transformers, convolutional networks to capture complex decision making.
- Architect and implement full-stack end-to-end navigation solutions, covering perception, prediction, and planning.
- Explore novel data generation and collection pipelines to enrich training datasets.
- Assist with deploying machine learning models into production environments
- Continuously monitor models in production, detecting model drift, and automating retraining processes as applicable
- Troubleshoot issues related to model deployment, performance, and system integration.
Machine Learning modeling
Model Deployment, Monitoring & Performance
What You Have
- Proficiency in Python and modern ML frameworks such as PyTorch, TensorFlow, or JAX, alongside working knowledge of C++ for deployment and system integration.
- Deep understanding of contemporary deep learning architectures, optimization, and evaluation, with a strong grasp of end-to-end navigation stack components — including perception, prediction, and path/motion planning.
- Proven track record deploying ML models into production environments, ideally within robotics, self-driving, or NLP.
Bachelor's or Master's degree in Computer Science, AI, Statistics, or a related field, with 4+ years of industry experience.
The Extras That Set You Apart
- Publications in top tier ML or robotics conferences
Skills Required
- Bachelor's or Master's degree in Computer Science, AI, Statistics, or related field
- 4+ years of industry experience
- Proficiency in Python
- Proficiency with modern ML frameworks such as PyTorch, TensorFlow, or JAX
- Working knowledge of C++ for deployment and system integration
- Deep understanding of deep learning architectures, optimization, and evaluation
- Experience with end-to-end navigation stack components (perception, prediction, path/motion planning)
- Proven track record deploying ML models into production, ideally in robotics, self-driving, or NLP
- Experience deploying, monitoring models in production, detecting drift, and automating retraining
- Publications in top-tier ML or robotics conferences
What We Do
FieldAI is pioneering the development of a field-proven, hardware agnostic brain technology that enables many different types of robots to operate autonomously in hazardous, offroad, and potentially harsh industrial settings – all without GPS, maps, or any pre-programmed routes.
