1.62 Machine learning Engineer en Field-ai

Who are We?Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications.Learn more at https://fieldai.com.About the JobAs an ML Engineer at Field AI, 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:

Machine Learning modeling:

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. 

Leverage imitation learning and reinforcement learning to advance planning and reasoning of our models.

Explore novel data generation and collection pipelines to enrich training datasets.

Model Deployment, Monitoring & Performance:

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.

What You Have:

Bachelor’s or Master’s degree in Computer Science, AI, Statistics, or related field.

Strong experience with Python and modern ML frameworks (PyTorch, TensorFlow, JAX).

Deep understanding of contemporary deep learning architectures, optimization, and evaluation.

Proven experience deploying ML models into production (robotics, self-driving, or NLP preferred).

Hands-on expertise in imitation learning and reinforcement learning.

Working knowledge of C++ for deployment and system integration.

The Extras That Set You Apart:

Publications in top tier ML or robotics conferences

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