Machine Learning Engineer (MLE)

Maxinsights; Santa Clara (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Maxinsights is seeking a Machine Learning Engineer to join the core team building scalable ML systems for real-world perception and embodied intelligence.

You will work across data collection, model training, evaluation, and deployment, collaborating with researchers, engineers, and product teams to turn complex data into robust ML solutions.

Qualifications

• Experience designing and deploying end-to-end ML systems.
• Strong understanding of deep learning and ML pipelines.
• Ability to optimize models for performance on real-world data.

Responsibilities

• Design, build, and own end-to-end machine learning systems from data exploration to deployment on large-scale data.
• Apply state-of-the-art ML techniques to new problem domains and optimize models and pipelines for production performance.
• Drive measurable improvements in model performance, system robustness, and product capabilities through applied ML.
• Collaborate with researchers, engineers, and product teams to translate research ideas into scalable ML solutions.
• Contribute to technical design, code quality, and best practices, shaping the ML platform's long-term direction.

Job description

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.

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.