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Hourly Remote Machine Learning Engineer Jobs in Missouri

DataAnnotation

Data Engineer - AI Trainer

Louisiana, MO · On-site +1

$60/hr

... remote work and setting your own schedule. We are looking for proficient programmers to help ... machine learning, and other engineers -- who are driving real‐world impact in AI development. Our ...

DataAnnotation

QA Engineer - AI Trainer

Louisiana, MO · On-site +1

$60/hr

... remote work and setting your own schedule. We are looking for proficient programmers to help ... full-stack, machine learning, and other engineers -- who are driving real-world impact in AI ...

Jobgether

Requirements The ideal candidate has a strong academic and practical background in machine learning ... Fully remote, global-first work environment * Opportunity to work on frontier AI research problems ...

DataAnnotation

UI Engineer - AI Trainer

California, MO · On-site +1

$60/hr

... remote work and setting your own schedule. We are looking for proficient programmers to help ... machine learning, and other engineers -- who are driving real‐world impact in AI development. Our ...

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What are the key skills and qualifications needed to thrive as an Hourly Remote Machine Learning Engineer, and why are they important?

To thrive as an Hourly Remote Machine Learning Engineer, you need strong programming skills (especially in Python), a solid understanding of machine learning algorithms, and experience with data preprocessing, typically supported by a relevant degree or equivalent experience. Familiarity with tools and frameworks such as TensorFlow, PyTorch, scikit-learn, cloud platforms (e.g., AWS, GCP), and version control systems like Git is essential. Excellent time management, self-motivation, and clear communication skills help you collaborate effectively across distributed teams and manage project-based work. These skills and qualities are vital for delivering high-quality results independently, meeting deadlines, and adapting to the dynamic needs of remote projects.

What are some common challenges faced by hourly remote machine learning engineers, and how can they be addressed?

Hourly remote machine learning engineers often encounter challenges such as managing time effectively across multiple projects, ensuring clear communication with distributed teams, and accessing necessary data or computing resources remotely. Building strong routines for regular check-ins and using collaborative tools can help maintain alignment with project goals. Additionally, proactively clarifying expectations and deliverables with clients or team leads can minimize misunderstandings and improve productivity in a remote, hourly environment.

What does an Hourly Remote Machine Learning Engineer do?

An Hourly Remote Machine Learning Engineer is a professional who develops and implements machine learning models and algorithms for clients or employers on an hourly contract basis, all while working from a remote location. Their responsibilities typically include data preprocessing, model selection, training, testing, and deployment. They collaborate with teams via online tools, manage their own schedules, and deliver results according to project requirements. This role allows for flexibility and the opportunity to work on diverse projects across different industries.
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What cities in Missouri are hiring for Hourly Remote Machine Learning Engineer jobs? Cities in Missouri with the most Hourly Remote Machine Learning Engineer job openings:
Hourly Remote Machine Learning Engineer jobs near you
Machine Learning Scientist - AI Trainer

Machine Learning Scientist - AI Trainer

DataAnnotation

California, MO • On-site, Remote

$40/hr

Full-time

Posted 18 days ago

Job description

Join the DataAnnotation team and contribute to developing cutting-edge AI systems, while enjoying the flexibility of remote work and setting your own schedule. We are looking for experienced quantitative professionals to help advance AI development. AI models are increasingly capable of performing complex analytical and scientific reasoning — but these systems still need practitioners with real-world quantitative experience to validate whether the outputs actually hold up in practice.

That's where you come in. As a member of DataAnnotation's team, you'll work closely with state-of-the-art AI models on tasks like evaluating AI-generated quantitative analysis, solving technical problems, and providing feedback that directly shapes how these systems reason about data, models, and scientific problems. Whether your background is in data science, astrophysics, economics, biostatistics, operations research, or any other quantitative field, if you think rigorously about data and models, your skills are directly applicable here.

Some team members fit this work alongside a full-time role, while others treat it as their primary focus. To get started, once you sign up for an account, you'll take a short assessment (this serves as our version of an interview). If you pass, you'll receive an email confirmation, and paid work will become available on our platform.

Benefits Fully remote: work from anywhere in the US, Canada, UK, Ireland, Australia, and New Zealand. Flexible schedule: choose which projects you take on and when you work. Competitive pay: projects are paid hourly, starting at $40+ USD per hour.

Impact: help shape the future of AI systems built to reason about data and analytics. Responsibilities Evaluate AI-generated quantitative work, including statistical analysis, predictive modeling, scientific reasoning, and data-driven insights, for technical accuracy and real-world validity. Design and solve quantitative problems used to train and benchmark AI systems, spanning areas like forecasting, experimental analysis, optimization, and statistical inference.

Write clear technical explanations and well-documented analytical code. Provide feedback that directly shapes the next generation of AI models built for quantitative reasoning. Qualifications 2+ years of hands‐on experience in a quantitative role or research environment — such as data science, statistics, economics, finance, physics, biology, epidemiology, operations research, or any adjacent field.

Some coding experience required, with comfort writing and reviewing analytical code end-to-end. Practical experience with statistical methods, predictive modeling, and experiment design (e.g., A/B testing, hypothesis testing, regression, classification, time‐series forecasting). Fluency in English (native or bilingual level) with strong writing skills.

A bachelor's degree in a quantitative field is preferred (Statistics, Computer Science, Mathematics, Engineering, or similar); a master's or PhD is a plus. Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or equivalent demonstrated expertise). Note: Payment is made via PayPal.

We will never ask for any money from you. This job is only available to those in the US, Canada, UK, Ireland, Australia, and New Zealand. #datascience #J-18808-Ljbffr

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