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Remote Google Machine Learning Engineer Jobs in Spring, TX

Remote Job Summary We are seeking experienced Senior Software Engineers to support an AI training project by creating reinforcement learning environments that evaluate AI models on complex software ...

New

Lead Machine Learning Engineer

Houston, TX ยท Remote

$104K - $138K/yr

As a Lead Machine Learning Engineer specializing in conversational and agentic systems at NobleAI ... Remote-first with co-working access at Industrious offices * 401(k) with employer match * Equity ...

Lead Machine Learning Engineer

Houston, TX ยท On-site +1

$97K - $128K/yr

As a Lead Machine Learning Engineer specializing in conversational and agentic systems at NobleAI ... Remote-first with co-working access at Industrious offices * 401(k) with employer match * Equity ...

Lead Machine Learning Engineer

Houston, TX ยท On-site +1

$97K - $128K/yr

As a Lead Machine Learning Engineer specializing in conversational and agentic systems at NobleAI ... Remote-first with co-working access at Industrious offices * 401(k) with employer match * Equity ...

AI/ML Engineer - Remote

Houston, TX ยท Remote

$200 - $350/hr

... machine learning applications. Key Responsibilities * Design, implement, and optimize AI/ML ... Deploy AI solutions across secure cloud environments such as AWS GovCloud, Google GovCloud, Azure ...

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Machine Learning Tutor

Houston, TX ยท Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Mental Health Expert - Remote

Houston, TX ยท Remote

$200 - $350/hr

AI/ML Engineer Job Type: Full-Time Location: Remote Job Summary We are seeking an experienced AI/ML ... machine learning applications. Key Responsibilities * Design, implement, and optimize AI/ML ...

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Software Engineer

Houston, TX ยท Remote

$60 - $75/hr

Software Engineer (Remote USA) | $60-$75 per Hour Do You Want to Build Software That Shapes the ... Background in machine learning, automation, or AI-driven applications. What's In It for You?

Software Engineer in Data Science

Houston, TX ยท On-site +1

$109K - $131K/yr

The individual will work both with our data scientists and machine learning engineers but will also need to directly engage with the commercial teams (across trading, operations, support functions ...

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Remote Google Machine Learning Engineer information

See Spring, TX salary details

$28K

$114.6K

$172.2K

How much do remote google machine learning engineer jobs pay per year?

As of Aug 30, 2026, the average yearly pay for remote google machine learning engineer in Spring, TX is $114,590.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,300.00 and $137,900.00 per year, depending on experience, location, and employer.

What is a remote Google machine learning engineer?

A Remote Google Machine Learning Engineer is a professional who designs, builds, and deploys machine learning models and artificial intelligence solutions, often using Google Cloud technologies, while working from a remote location. These engineers collaborate with cross-functional teams to solve complex business problems, optimize data pipelines, and improve model performance. Their responsibilities typically include data preprocessing, model selection, training, evaluation, and deployment, all while ensuring scalability and security. Working remotely allows them to contribute to projects from anywhere, leveraging cloud-based tools and collaboration platforms.

What are the key skills and qualifications needed to thrive as a remote Google machine learning engineer?

To thrive as a Remote Google Machine Learning Engineer, you need a strong background in computer science, mathematics, and machine learning algorithms, typically supported by a relevant degree and experience in building scalable models. Proficiency with tools such as TensorFlow, Python, Google Cloud Platform (GCP), and familiarity with distributed systems is essential. Excellent problem-solving, communication, and self-management skills are crucial for effective remote collaboration and innovation. These capabilities enable engineers to deliver impactful machine learning solutions while seamlessly integrating with global Google teams.

How do remote Google machine learning engineers typically collaborate with cross-functional teams while working from different locations?

Remote Google Machine Learning Engineers often use a combination of video conferencing, cloud-based collaboration tools, and shared code repositories to work closely with data scientists, product managers, and software engineers. Regular stand-up meetings, sprint planning sessions, and detailed documentation help ensure everyone is aligned and project milestones are met. Despite being remote, engineers are encouraged to proactively communicate progress, share insights, and participate in code reviews to maintain a strong team dynamic and drive successful project outcomes.

What are popular job titles related to Remote Google Machine Learning Engineer jobs in Spring, TX?

For Remote Google Machine Learning Engineer jobs in Spring, TX, the most frequently searched job titles are:

What job categories do people searching Remote Google Machine Learning Engineer jobs in Spring, TX look for?

The top searched job categories for Remote Google Machine Learning Engineer jobs in Spring, TX are:

What cities near Spring, TX are hiring for Remote Google Machine Learning Engineer jobs?

Cities near Spring, TX with the most Remote Google Machine Learning Engineer job openings:

Machine Learning Engineer - Remote

YO AI Labs

Houston, TX โ€ข Remote

$80 - $120/hr

Full-time

Posted 3 days ago

New


Job description

Senior Software Engineer

Job Type: Contractor (~15 hours/week)
Location: Remote

Job Summary

We are seeking experienced Senior Software Engineers to support an AI training project by creating reinforcement learning environments that evaluate AI models on complex software engineering tasks using Model Context Protocol (MCP) tools.

You will design reproducible environments, deterministic verification, and reference solutions for tasks such as bug fixing, feature implementation, codebase refactoring, and performance optimization. No prior AI experience is required.

Key Responsibilities
  • Create reinforcement learning environments for software engineering tasks.
  • Design tasks involving bug fixing, feature development, refactoring, and performance optimization.
  • Build deterministic verification systems and golden reference solutions.
  • Evaluate AI agents' ability to reason through complex codebases and use MCP tools effectively.
  • Develop realistic, reproducible software engineering scenarios.
  • Ensure tasks accurately measure coding ability, problem-solving, and tool usage.
  • Document solutions and provide clear technical feedback.
Required Skills
  • Strong proficiency in Python 3, Java, Rust, C++, or TypeScript.
  • Strong understanding of algorithms and data structures.
  • Experience with bug fixing and debugging complex software issues.
  • Proven experience in feature implementation and codebase refactoring.
  • Strong knowledge of performance optimization and tuning.
  • Excellent written and verbal communication.
  • Strong attention to detail.
Preferred Qualifications
  • Experience working with large-scale or distributed codebases.
  • Familiarity with AI/ML systems is a plus but not required.
  • Experience with rigorous code reviews and software engineering best practices.
  • Experience working effectively in remote or cross-functional teams.
Hiring Process
  1. Submit an application and screening questions.
  2. Complete an AI interview (~30 minutes).
  3. Complete a technical assessment, if required.
  4. Hiring Manager review.
Compensation

Compensation is output-based, with payment provided per task that meets project specifications. Minimum weekly submission requirements may apply.

Availability

Selected experts should be prepared to begin their first tasks within 24–48 hours of completing onboarding.