Machine Learning Engineer, Gecko Robotics

• Bachelor’s degree in Computer Science or a closely related field, or equivalent practical experience
• 5+ years of professional engineering experience, with at least 3+ years in a dedicated machine learning role
• Strong practical knowledge of machine learning algorithms and the ability to read and implement relevant research papers, especially for time-series analysis and anomaly detection in signal data
• Proficiency in Python and at least one major machine learning framework (e.g., PyTorch, TensorFlow), with experience deploying at least one model into a production environment
• Familiarity with MLOps concepts for managing the model lifecycle
• A strong sense of intellectual curiosity, with the desire to dive deep into exploratory projects alongside production-ready deployments
• Preference for projects with high ownership and the ability to work effectively both autonomously and collaboratively
• Desire to have a high impact at a fast-moving startup as a key contributor on a new and growing team
• Exceptional communication skills and a commitment to receiving and providing continuous feedback
• Deep theoretical understanding and applied expertise in modern machine learning, including deep learning architectures (e.g., CNNs, Transformers)
• Demonstrated ability to tackle complex, ambiguous problems and work with noisy, real-world data

Desirable

• As a Machine Learning Engineer at Gecko, you will be working with Gecko’s unique dataset to develop and deploy machine learning models to solve critical business problems
• You will work deeply on problems such as: classifying valid vs invalid signals; taking accurate measurements from valid signals; and identifying damage mechanisms such as cracks, corrosion, or laminations across a large collection of signals
• Gecko owns a growing repository of mechanical integrity data, including large volumes of ultrasonic, imagery, and other data points concerning the integrity of critical infrastructure assets worldwide
• Gecko is expanding our Machine Learning team to better leverage the vast data store we have collected over time
• This is a chance to be one of the first dedicated engineers in an area we expect to grow significantly over the next few years, within a space that is ripe for innovation and solving problems in a way they haven’t been attempted before
• You will be a key member of our advanced technology team, responsible for pioneering novel machine learning models that interpret complex sensor data from our robotic inspection platforms
• This hybrid role bridges the gap between state-of-the-art research and practical engineering
• You'll design, develop, and deploy sophisticated machine learning models (supervised and unsupervised) to analyze complex sensor data for non-destructive evaluation (NDE)
• Lead research and implementation of next-generation, physics-based machine learning models that can interface with or replace traditional simulation methods like Finite Element Analysis (FEA) or Computational Fluid Dynamics (CFD)
• Process and interpret large-scale, unconventional datasets, including raw ultrasonic A-scans and infrared thermal images, to extract meaningful insights
• Collaborate with robotics, software, and data engineering teams to integrate ML solutions and algorithms into our production data platform
• Stay at the forefront of academic and industry advancements in machine learning, signal processing, and physics-based modeling to drive innovation
• Own the end-to-end lifecycle of ML projects, from problem formulation and data exploration and curation to model deployment, monitoring, and iteration
• Technologies We Use:
• Python, PyTorch, Numpy, Scipy, Pandas, Scikit-learn
• Docker, K8s, GCP, Cloud Run, Batch, Collab
• Ultrasonic DSP
• We use a variety of technologies, but our Software teams primarily operate using Python, React, and Typescript with Google Cloud Platform (GCP) as our cloud provider. This is a non-exhaustive list and we are tech agnostic in our interview process, so we encourage you to apply regardless of your background