Machine Learning Engineer

Glint Tech Solutions LLC•Santa Clara, CA

About The Position

We are looking for a Machine Learning Engineer to join our core team building scalable ML systems for real-world perception and embodied intelligence. In this role, you will work on end-to-end machine learning systems, spanning data collection, model training, evaluation, and deployment. You will collaborate closely with researchers, engineers, and product teams to turn complex real-world data into robust, production-ready ML solutions. This role is well-suited for engineers who enjoy working across the ML stack, are comfortable operating in ambiguous problem spaces, and are excited about applying modern deep learning methods to real-world perception, human-centric, and embodied AI problems.

Requirements

Bachelor’s, Master’s, or PhD in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience.
3+ years of experience building and shipping machine learning systems.
Strong proficiency in Python and experience with at least one major deep learning framework (e.g., PyTorch, TensorFlow).
Solid understanding of modern deep learning concepts, training workflows, model evaluation, and experience working with real-world, production-oriented ML pipelines.
Strong problem-solving skills and ability to work effectively in a fast-moving, collaborative environment.

Nice To Haves

PhD in a relevant field with a research focus in robot learning, embodied AI, or visual perception.
Experience with end-to-end ML systems, including data collection, training, inference, and deployment.
Background in computer vision, perception, or multi-modal machine learning, including egocentric or human-centric perception.
Familiarity with large-scale training, experimentation infrastructure, or production ML systems.
Ability and interest in learning new problem domains, data modalities, and ML techniques quickly.
Publications in leading venues, open-source contributions, or demonstrated impact in applied ML or AI systems.

Responsibilities

Design, build, and own end-to-end machine learning systems, from data exploration and model development to evaluation and deployment on large-scale, real-world data.
Apply state-of-the-art ML techniques to new problem domains and optimize models and pipelines for performance, efficiency, and reliability in production environments.
Drive measurable improvements in model performance, system robustness, and product capabilities through applied machine learning.
Collaborate closely with cross-functional teams to translate research ideas and product requirements into scalable ML solutions.
Contribute to technical design, code quality, and best practices, and help shape the long- term direction of the company’s machine learning platform.