Machine Learning Engineer

What We Do

Gecko Robotics is helping the world’s most important organizations ensure the availability, reliability, and sustainability of critical infrastructure. Gecko's complete and connected solutions combine wall-climbing robots, industry-leading sensors, and an AI-powered data platform to provide customers with a unique window into the current and future health of their physical assets. This enables real-time decision making to increase the efficiency and safety of operations, promote mission readiness, and protect the environment and civilization from the effects of infrastructure failure.

Role at a Glance

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.

What you will do

• 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 modeldeployment, 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.

About You

Required Skills

• 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.

Preferred Skills

• Experience with PyTorch.

• Experience with MLOps tools such as MLFlow.

• A strong background in digital signal processing (DSP).

• Experience setting up and interpreting the results of FEA/ CFD simulations and the like.

• Experience working with unconventional sensor data formats beyond standard images or text.

• Experience or a keen research interest in the intersection of machine learning and physical sciences.

• Familiarity with research related to ultrasound A-scans, such as time-series analysis, wavelet transforms, Fourier analysis, or acoustic modeling.

• Familiarity with research related to infrared imagery, such as radiometry, thermal modeling, or multispectral data fusion.

• Familiarity with applying machine learning to the simulation of physical phenomena, including research areas such asphysics-informed neural networks (PINNs), surrogate modelingfor traditional simulators (e.g., FEA/CFD), or prognostic and health management (PHM) for modeling material degradation and damage, neural operators, differentiable physics and the likes.

• Experience with 3D deep learning for processing point cloud data, including tasks like 3D semantic segmentation. Familiarity with modern neural rendering techniques like Neural Radiance Fields (NeRFs) or3D Gaussian Splatting and knowledge of generative models for inverse CAD to automatically create structured CAD models from 3D scans.

Who We Are

At Gecko, our people are our greatest investment. In addition to competitive compensation packages, we offer company equity, 401(k) matching, gender-neutral parental leave, full medical, dental, and vision insurance, mental health and wellness support, ongoing professional development, family planning assistance, and flexible paid time off. 

Gecko values collaboration, innovation, and partnership, and we believe we do our best work when we're together in person. We’re an office-first culture but understand that sometimes you may need to work from home. Many people are in the office five days a week, others need a bit more flexibility. Ultimately, we care about the outcomes we achieve - and creating a culture of autonomy and trust that enables that impact.

Gecko is committed to creating a culture of inclusion and belonging, and we are proud to be an equal opportunity employer.  We believe it is our collective responsibility to uphold these values and encourage candidates from all backgrounds to join us in our mission to protect today’s infrastructure and give form to tomorrow’s. All qualified applicants will be treated with respect and receive equal consideration for employment without regard to race, color, creed, religion, sex, gender identity, sexual orientation, national origin, disability, uniform service, veteran status, age, or any other protected characteristic per federal, state, or local law. If you are passionate about what you do and want to use your talents to support our critical mission, we’d love to hear from you.