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Freelance Google Machine Learning Engineer Jobs (NOW HIRING)

10a Labs

Machine Learning Engineer

Washington, DC · On-site

$130K - $200K/yr

We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build ... Experience with cloud platforms such as Google Cloud Platform (preferred), AWS, or Azure, including ...

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a Platform-as-a-Service (PaaS), allowing industry partners to customize, localize and integrate search ...

NTENT

Machine Learning Engineer

Carlsbad, CA

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a Platform-as-a-Service (PaaS), allowing industry partners to customize, localize and integrate search ...

Spotify

Machine Learning Engineer

They are seeking a Machine Learning Engineer to build systems that analyze the performance of music promotions, providing actionable insights for creators and partners. Responsibilities : • ...

Eccalon LLC

Machine Learning Engineer

Detroit, MI

Machine Learning Engineer Location: Detroit, MI- Onsite Type: Full-time Security Clearance: No clearance required, must be clearable. The Machine Learning Engineer will be an essential member of the ...

Spotify

Machine Learning Engineer

Spotify is a leading music streaming platform, and they are seeking a Machine Learning Engineer to join their Music Promotion team. The role involves building systems to understand the performance of ...

Cymertek

Machine Learning Engineer

San Antonio, TX · On-site

Familiarity with cloud-based platforms for machine learning (e.g., AWS, Google Cloud, Azure) * Strong problem-solving skills and analytical thinking REQUIRED SKILLS * Proficiency in programming ...

Chefman

Machine Learning Engineer

Mahwah, NJ

As a Machine Learning Engineer, you will play a critical role in shaping the future of cooking ... Experience working with public cloud platforms such as AWS, Google Cloud Platform, or Microsoft ...

Software Technology Inc

Machine Learning Engineer

$117K - $140K/yr

Machine Learning Engineer Apex Systems has an opening for a Remote Machine Learning Engineer ... Cloudera Data Science Workbench, Azure ML, Amazon ML, Google AutoML, Vertex AI. * Data ...

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Freelance Google Machine Learning Engineer information

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How much do freelance google machine learning engineer jobs pay per hour?

As of Jun 26, 2026, the average hourly pay for freelance google machine learning engineer in the United States is $47.71, according to ZipRecruiter salary data. Most workers in this role earn between $24.28 and $61.78 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Freelance Google Machine Learning Engineer, and why are they important?

To thrive as a Freelance Google Machine Learning Engineer, you need a solid background in computer science, statistics, and machine learning, typically supported by a relevant degree and experience with real-world data projects. Familiarity with Google Cloud Platform (GCP), TensorFlow, and certifications like Google Professional Machine Learning Engineer are commonly required. Strong problem-solving abilities, self-motivation, and effective client communication distinguish top freelancers in this field. These skills and qualifications are crucial for delivering robust machine learning solutions tailored to client needs and efficiently navigating remote, project-based work.

What does a Freelance Google Machine Learning Engineer do?

A Freelance Google Machine Learning Engineer is a technical specialist who designs, develops, and deploys machine learning models using Google’s tools and platforms, such as TensorFlow and Google Cloud AI services. They work independently or with clients to solve data-driven problems, build predictive models, and automate processes using machine learning techniques. Their responsibilities may include data preprocessing, feature engineering, model training and evaluation, and integrating models into production systems. Freelancers often manage multiple projects and must stay updated on the latest ML advancements and Google technologies.

What are some common challenges freelance Google Machine Learning Engineers face when working with clients remotely?

Freelance Google Machine Learning Engineers often encounter challenges such as clearly defining project scopes, aligning on deliverables, and managing expectations, especially when working remotely. Communication can be more complex due to time zone differences and varying levels of technical understanding among clients. Staying updated with Google’s latest ML tools and ensuring secure, efficient data sharing are also important. Building strong documentation and regular progress updates can help foster trust and smooth collaboration.

What is the difference between Freelance Google Machine Learning Engineer vs Freelance Data Scientist?

AspectFreelance Google Machine Learning EngineerFreelance Data Scientist
CredentialsKnowledge of Google Cloud ML tools, programming skills in Python, TensorFlowStatistical expertise, programming in Python/R, data analysis skills
Work EnvironmentCloud platforms, AI/ML projects, collaboration with developersData analysis, reporting, model development, client communication
Industry UsageTech companies, AI startups, cloud service providersFinance, healthcare, marketing, research organizations

While both roles involve working with data and models, a Freelance Google Machine Learning Engineer specializes in deploying ML solutions on Google Cloud, focusing on AI/ML engineering tasks. A Freelance Data Scientist primarily analyzes data, builds statistical models, and provides insights. The roles overlap in skills but differ in focus and tools used.

More about Freelance Google Machine Learning Engineer jobs
What cities are hiring for Freelance Google Machine Learning Engineer jobs? Cities with the most Freelance Google Machine Learning Engineer job openings:
What are the most commonly searched types of Google Machine Learning Engineer jobs? The most popular types of Google Machine Learning Engineer jobs are:
What states have the most Freelance Google Machine Learning Engineer jobs? States with the most job openings for Freelance Google Machine Learning Engineer jobs include:
What job categories do people searching Freelance Google Machine Learning Engineer jobs look for? The top searched job categories for Freelance Google Machine Learning Engineer jobs are:
Freelance Google Machine Learning Engineer jobs near you
Infographic showing various Freelance Google Machine Learning Engineer job openings in the United States as of June 2026, with employment types broken down into 97% Full Time, 1% Part Time, and 2% Nights. Highlights an 77% Physical, 2% Hybrid, and 21% Remote job distribution, with an average salary of $99,230 per year, or $47.7 per hour.

Machine Learning Engineer

10a Labs

Washington, DC • On-site

$130K - $200K/yr

Full-time

Medical, Dental, Vision, PTO

Posted 23 days ago

Job description

About 10a Labs: 10a Labs is the safety and threat-intelligence layer trusted by frontier AI labs, AI unicorns, Fortune 10 companies, and leading global technology platforms. Our adversarial red teaming, model evaluations, and intelligence collection enable engineering, safety, and security teams to stay ahead of evolving threats and deploy AI systems safely.
About the Role:
We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build, evaluate, and deploy advanced machine learning systems across a range of safety, security, and intelligence applications.
This role spans the full ML lifecycle, from dataset development and experimentation to model training, evaluation, deployment, and monitoring. You will work both independently and collaboratively across projects involving multimodal classification systems, frontier model evaluations, model distillation research, and agentic workflows. The ideal candidate combines strong engineering fundamentals with a research mindset and enjoys tackling ambiguous, high-impact problems at the frontier of AI.
You will collaborate closely with researchers, software engineers, red teamers, and subject-matter experts to develop production-ready systems that support leading AI organizations and technology companies.
Responsibilities may include:
  • Design, train, evaluate, and deploy machine learning models across text, image, audio, and multimodal domains.
  • Develop and improve classification systems for safety, security, abuse detection, and intelligence applications.
  • Conduct experiments to benchmark, evaluate, and compare AI models, including large language models and multimodal systems.
  • Contribute to model distillation, optimization, and fine-tuning efforts to improve performance, efficiency, and deployability.
  • Design evaluation pipelines, metrics, and testing frameworks to measure model capabilities, reliability, and safety.
  • Build agentic systems and automated workflows for evaluation, red teaming, research, and large-scale experimentation.
  • Own ML projects from initial research and prototyping through production deployment and monitoring.
  • Partner with software engineers to productionize ML systems and support ongoing improvements.
  • Provide technical expertise and guidance across client engagements and internal research initiatives.

We're looking for someone who:
  • Brings curiosity, creativity, and rigor to ambiguous research and engineering problems, with a bias toward experimentation and rapid iteration;
  • Thrives in collaborative, interdisciplinary environments while also being comfortable independently driving projects to completion;
  • Communicates technical concepts clearly to both technical and non-technical audiences;
  • Is resourceful, proactive, and comfortable operating in a fast-moving startup environment.
  • Is excited about developing novel approaches that advance the state of AI safety, evaluation, and security.

Requirements:
  • 3-5+ years of professional experience building and deploying machine learning systems.
  • Strong proficiency in Python and modern machine learning frameworks such as PyTorch and/or TensorFlow
  • Experience working across multiple modalities, with expertise in one or more of:
    • Computer Vision: image classification, object detection, OCR, segmentation, deepfake detection, multimodal vision-language systems, or related areas.
    • Natural Language Processing: LLMs, text classification, information extraction, retrieval systems, speech-to-text, agentic applications, or related areas.
  • Experience training, fine-tuning, evaluating, and deploying machine learning models in production environments.
  • Experience designing evaluation methodologies, benchmarking systems, and model performance metrics.
  • Experience with MLOps tools and practices (Docker, Kubernetes, CI/CD for ML, MLflow, etc.)
  • Experience with cloud platforms such as Google Cloud Platform (preferred), AWS, or Azure, including ML infrastructure, workflow orchestration, storage, and database services.
  • Familiarity or experience with model distillation, synthetic data generation, reinforcement learning, or AI evaluation research is strongly preferred.

Preferred:
Experience working with frontier language models, multimodal foundation models, or AI safety evaluations.Prior experience in cybersecurity, trust and safety, abuse prevention, threat intelligence, or related domains.Experience with retrieval-augmented generation (RAG), AI agent frameworks, and context orchestration systems such as LangChain, LlamaIndex, OpenAI Agents, or AutoGen.
Compensation:
  • Salary Range: $130K-$200K, depending on experience and location
  • Bonus: Performance-based annual bonus
  • Professional Development: Support for conferences, continuing education, or leadership training
  • Work Environment: Fully remote, U.S.-based
  • Health Benefits: Comprehensive health, dental, and vision coverage

Time Off: Generous PTO and paid holiday schedule

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