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Remote Deep Learning Jobs in Missouri (NOW HIRING)

Jobgether

The role combines deep theoretical research with hands-on system development and experimentation ... Fully remote, global-first work environment * Opportunity to work on frontier AI research problems ...

EnthuZiastic

Growth Hacker - Remote

Kansas City, MO · Remote

Proven experience in digital marketing, with a deep understanding of various marketing channels and ... Continuous Learning: * A commitment to ongoing learning and professional development in growth ...

EnthuZiastic

Growth Hacker - Remote

Columbia, MO · Remote

Proven experience in digital marketing, with a deep understanding of various marketing channels and ... Continuous Learning: * A commitment to ongoing learning and professional development in growth ...

EnthuZiastic

Growth Hacker - Remote

Springfield, MO · Remote

Proven experience in digital marketing, with a deep understanding of various marketing channels and ... Continuous Learning: * A commitment to ongoing learning and professional development in growth ...

EnthuZiastic

Growth Hacker - Remote

Saint Louis, MO · Remote

Proven experience in digital marketing, with a deep understanding of various marketing channels and ... Continuous Learning: * A commitment to ongoing learning and professional development in growth ...

EnthuZiastic

Growth Hacker - Remote

Independence, MO · Remote

Proven experience in digital marketing, with a deep understanding of various marketing channels and ... Continuous Learning: * A commitment to ongoing learning and professional development in growth ...

EnthuZiastic

Growth Hacker - Remote

Jefferson City, MO · Remote

Proven experience in digital marketing, with a deep understanding of various marketing channels and ... Continuous Learning: * A commitment to ongoing learning and professional development in growth ...

Koantek

Senior AI Engineer

Chesterfield, MO · Remote

$54.75 - $70.50/hr

This remote role requires a blend of advanced Machine Learning (ML) expertise, deep knowledge of MLOps principles, and a proven track record in client-facing implementation. The successful candidate ...

Jobgether

... learning solutions. * Deep expertise with at least one major cloud platform such as Google Cloud ... Fully remote work environment with flexibility for candidates based in the EU or United Kingdom.

Jobgether

... deep technical problem-solving. The role offers strong autonomy, exposure to cutting-edge AI ... Remote flexibility and continuous learning are core aspects of the working culture.

Jobgether

The position combines educational impact with flexibility, offering a fully remote and part-time ... Deep understanding of admissions processes and expectations at highly selective UK institutions.

Jobgether

$45K - $60K/yr

Develop deep expertise in the platform's ecosystem, tools, and integrations to troubleshoot a wide ... Home office stipend to help create an effective remote workspace. * Learning and development budget ...

Jobgether

$80.20K - $110.30K/yr

The position is fully remote within Europe and includes limited international travel ... Develop, coach, and strengthen a high-performing TA organization by implementing targeted learning ...

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All JobsDeep Learning JobsRemote Deep Learning JobsRemote Deep Learning Jobs in Missouri

Remote Deep Learning information

See Missouri salary details

$23

$53

$80

How much do remote deep learning jobs pay per hour?

As of May 29, 2026, the average hourly pay for remote deep learning in Missouri is $53.09, according to ZipRecruiter salary data. Most workers in this role earn between $42.98 and $65.72 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Remote Deep Learning Engineer, and why are they important?

To thrive as a Remote Deep Learning Engineer, you need strong programming skills in Python, a deep understanding of machine learning algorithms, and typically a degree in computer science, engineering, or a related field. Proficiency with frameworks like TensorFlow or PyTorch, as well as cloud computing platforms such as AWS or Google Cloud, is essential, and certifications in these technologies can be advantageous. Excellent problem-solving abilities, self-motivation, and clear communication are crucial soft skills for remote collaboration and project delivery. These skills ensure effective development, deployment, and maintenance of deep learning models while working independently in distributed teams.

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

Remote deep learning engineers often encounter challenges such as limited access to high-performance computing resources, communication barriers with distributed teams, and difficulties in collaborating on large codebases or datasets. These issues can be mitigated by leveraging cloud-based platforms for scalable computing, using clear communication tools like Slack or Zoom for regular check-ins, and employing version control systems like Git for collaborative code management. Proactively setting up workflows and documentation helps ensure smooth collaboration and project continuity within a remote environment.

What is a Remote Deep Learning job?

A Remote Deep Learning job involves working with artificial intelligence and machine learning models, particularly using deep neural networks, from a location outside a traditional office, often from home. Professionals in this field design, build, and optimize algorithms that enable computers to learn from large amounts of data. They often work on projects such as image and speech recognition, natural language processing, or autonomous systems. The remote aspect allows flexibility and access to global opportunities, but requires strong communication skills and the ability to collaborate virtually with teams.

What is the difference between Remote Deep Learning vs Remote Machine Learning Engineer?

AspectRemote Deep LearningRemote Machine Learning Engineer
Required CredentialsBachelor's/Master's in CS, AI, or related; experience with neural networksBachelor's/Master's in CS, Data Science, or related; experience with algorithms and data modeling
Work EnvironmentCollaborative teams, research-focused, often in tech or AI companiesDevelopment teams, data-driven projects, across various industries
Employer & Industry UsageTech firms, AI startups, research institutionsTech companies, finance, healthcare, e-commerce

Remote Deep Learning specialists focus on designing and training neural networks for AI applications, often requiring advanced knowledge of deep neural architectures. Remote Machine Learning Engineers work on developing algorithms and models for broader data analysis and predictive tasks. While both roles involve machine learning, deep learning emphasizes neural networks, whereas machine learning engineers may work with a variety of algorithms across industries.

What are popular job titles related to Remote Deep Learning jobs in Missouri? For Remote Deep Learning jobs in Missouri, the most frequently searched job titles are:
What job categories do people searching Remote Deep Learning jobs in Missouri look for? The top searched job categories for Remote Deep Learning jobs in Missouri are:
What cities in Missouri are hiring for Remote Deep Learning jobs? Cities in Missouri with the most Remote Deep Learning job openings:
Remote Deep Learning jobs near you
Infographic showing various Remote Deep Learning job openings in Missouri as of May 2026, with employment types broken down into 69% Full Time, 28% Part Time, 1% Temporary, and 2% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $110,433 per year, or $53.1 per hour.

AI Research Engineer (Multi-Modal Reinforcement Learning)

Jobgether

Remote

Full-time

Posted 9 days ago

Job description

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a AI Research Engineer (Multi-Modal Reinforcement Learning) in Netherlands.

This role sits at the intersection of cutting-edge AI research and large-scale system engineering, focusing on advancing multi-modal reinforcement learning across text, image, audio, and complex simulated environments. You will contribute to the design of next-generation intelligent systems capable of adaptive decision-making in real-world scenarios. Working in a highly research-driven, globally distributed environment, you will help build and scale reinforcement learning frameworks that power advanced multimodal models. Your work will directly influence model performance, training stability, and reward optimization strategies at scale. You will collaborate with top-tier researchers and engineers to push the boundaries of AI capabilities. The role combines deep theoretical research with hands-on system development and experimentation. It is ideal for someone passionate about foundational AI breakthroughs and real-world deployment impact.

Accountabilities

In this role, you will lead research and engineering efforts across multi-modal reinforcement learning systems while contributing to scalable AI infrastructure and experimentation frameworks. You will be responsible for advancing model performance and robustness through innovative algorithm design and rigorous evaluation practices.

  • Conduct research on reinforcement learning methods for multi-modal systems, including diffusion-based and autoregressive model approaches.
  • Design and build scalable RL infrastructure supporting distributed training and evaluation across complex multi-modal environments.
  • Develop reward modeling strategies to improve alignment, training stability, and mitigate failure modes such as reward hacking.
  • Create and curate simulation environments and datasets for training, benchmarking, and validating multi-modal RL models.
  • Design and execute evaluation protocols to measure performance improvements and ensure reproducibility across experiments.
  • Analyze model behavior across modalities, identifying bottlenecks in optimization, exploration, and cross-modal alignment.
  • Explore and develop next-generation RL paradigms to enhance learning from environment feedback and improve SOTA performance.
  • Publish research in leading AI conferences such as NeurIPS, ICML, ICLR, CVPR, and related venues.
Requirements

The ideal candidate has a strong academic and practical background in machine learning, reinforcement learning, and multi-modal AI systems, with a proven record of research excellence and scalable system development. You are comfortable working at the frontier of AI research while building production-grade experimentation pipelines.

  • Master's degree in Computer Science or related field required; PhD preferred in ML, CV, NLP, or AI-related disciplines.
  • Strong publication record in top-tier AI conferences (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV).
  • Proven experience in large-scale reinforcement learning experiments, particularly in multi-modal or vision-centric systems.
  • Deep understanding of reinforcement learning theory, optimization, and policy learning in high-dimensional environments.
  • Strong hands-on experience with PyTorch and deep learning frameworks for multimodal AI systems.
  • Experience building end-to-end RL pipelines including simulation, training, evaluation, and deployment.
  • Ability to address core RL challenges such as sample efficiency, exploration-exploitation trade-offs, and training stability.
  • Strong analytical and problem-solving skills with a research-driven, experimental mindset.
Benefits
  • Competitive compensation package aligned with top-tier AI research talent
  • Fully remote, global-first work environment
  • Opportunity to work on frontier AI research problems at scale
  • High-impact role influencing next-generation multimodal intelligence systems
  • Collaboration with leading researchers and engineers in AI and reinforcement learning
  • Access to large-scale experimentation infrastructure and research resources
  • Strong culture of innovation, autonomy, and research publication support
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
 Why Apply Through Jobgether? 
 
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
 
 
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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