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Research Assistant Deep Learning Jobs (NOW HIRING)

Occulytics Inc.

Deep Learning Engineer

... deep learning research, experiment with novel architectures and techniques, and translate cutting-edge academic research into practical solutions for senior living market intelligence. • Deploy ...

PlusAI

Deep Learning Intern

Santa Clara, CA

$19 - $65/hr

Develop and benchmark cutting-edge techniques in deep learning. * Collaborate with team members to ... These tools assist our recruitment team but do not replace human judgment. Final hiring decisions ...

Matroid

Deep Learning Engineer

Palo Alto, CA · On-site

$170K - $300K/yr

Keep on top of the latest developments and research in academic CV/DL and decide how we should ... Passion for computer vision and deep learning; you are excited to adapt the latest multimodal LLMs ...

E Ink

Deep Learning Researcher 1900S-2

Billerica, MA · Hybrid

$130K - $160K/yr

The Billerica Research & Development team is thriving and growing as we help develop products that ... Develop and evaluate novel deep learning models for complex physical and chemical systems in ...

Matroid

Deep Learning Engineer

Palo Alto, CA

Keep on top of the latest developments and research in academic CV/DL and decide how we should ... Passion for computer vision and deep learning; you are excited to adapt the latest multimodal LLMs ...

E Ink

Deep Learning Researcher 1900S-2

Billerica, MA · On-site

$130K - $160K/yr

The Billerica Research & Development team is thriving and growing as we help develop products that ... Develop and evaluate novel deep learning models for complex physical and chemical systems in ...

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How much do research assistant deep learning jobs pay per hour?

As of May 31, 2026, the average hourly pay for research assistant deep learning in the United States is $21.91, according to ZipRecruiter salary data. Most workers in this role earn between $18.51 and $25.48 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Research Assistant in Deep Learning, and why are they important?

To thrive as a Research Assistant in Deep Learning, you need a strong background in machine learning, programming (especially Python), and a relevant degree in computer science or a related field. Familiarity with deep learning frameworks such as TensorFlow or PyTorch, as well as experience with data preprocessing and GPU computing, are typically required. Strong analytical thinking, attention to detail, and effective communication skills help you excel in collaborative research environments. These skills and qualities are essential for efficiently developing, testing, and improving advanced machine learning models in a fast-evolving field.

What types of projects and daily tasks can a Research Assistant in Deep Learning expect to work on?

As a Research Assistant in Deep Learning, you can expect to work closely with research scientists and engineers to design, implement, and evaluate novel deep learning models. Typical daily tasks include data preprocessing, running experiments, analyzing results, and contributing to academic papers or presentations. You may also assist in developing codebases, conducting literature reviews, and collaborating with team members to solve technical challenges. The work environment is often collaborative and fast-paced, with opportunities to learn from experts and contribute to cutting-edge research projects.

What are Research Assistant Deep Learning jobs?

Research Assistant Deep Learning jobs involve supporting research projects focused on artificial intelligence, specifically within the field of deep learning. These roles typically require assisting with data collection, preprocessing, running machine learning experiments, and analyzing results. Research assistants may also help with literature reviews, code development, and documentation. The position is often found in academic, industry, or research lab settings, and usually requires a solid foundation in programming, mathematics, and neural network concepts.

What is the difference between Research Assistant Deep Learning vs Research Assistant Machine Learning?

AspectResearch Assistant Deep LearningResearch Assistant Machine Learning
Required CredentialsBachelor's or Master's in Computer Science, Data Science, or related fields; knowledge of neural networksBachelor's or Master's in Computer Science, Data Science, or related fields; foundational ML knowledge
Work EnvironmentResearch labs, universities, tech companies focusing on AI and neural networksResearch labs, universities, tech companies working on various ML algorithms
Employer & Industry UsageAI research, deep learning projects, neural network developmentGeneral machine learning applications, data analysis, predictive modeling

Research Assistant Deep Learning specializes in neural networks and AI-focused projects, while Research Assistant Machine Learning covers a broader range of algorithms and data analysis tasks. Both roles require similar educational backgrounds but differ in technical focus and application areas.

More about Research Assistant Deep Learning jobs
What cities are hiring for Research Assistant Deep Learning jobs? Cities with the most Research Assistant Deep Learning job openings:
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Research Assistant Deep Learning jobs near you
Infographic showing various Research Assistant Deep Learning job openings in the United States as of May 2026, with employment types broken down into 3% As Needed, 55% Full Time, 39% Part Time, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $45,571 per year, or $21.9 per hour.

Deep Learning Engineer

Occulytics Inc.

Remote

Full-time

Posted 5 days ago

Job description

Job Summary:
Occulytics Inc. is an innovative startup on a mission to fundamentally change the senior living industry. They are seeking a Deep Learning Engineer to build and deploy cutting-edge deep learning models that transform market intelligence and drive data-driven decision making across the industry.
Responsibilities:
• Design, implement, and optimize deep learning architectures for predictive modeling, market forecasting, customer segmentation, and lead scoring to extract actionable insights from complex demographic and behavioral datasets in the senior living industry.
• Build robust, scalable data pipelines for training and inference, ensuring efficient data preprocessing, feature engineering, and model deployment workflows that handle large-scale healthcare and demographic datasets.
• Stay current with latest developments in deep learning research, experiment with novel architectures and techniques, and translate cutting-edge academic research into practical solutions for senior living market intelligence.
• Deploy models to production environments with monitoring, versioning, and automated retraining capabilities. Collaborate with engineering teams to integrate ML capabilities into customer-facing products and internal analytics platforms.
• Partner with data scientists, product managers, and domain experts to understand business requirements, translate them into technical specifications, and communicate model performance and insights to non-technical stakeholders.
Qualifications:
Required:
• 3+ years of hands-on experience developing and deploying deep learning models in production environments.
• Strong portfolio of ML projects demonstrating technical depth and practical application.
• Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow).
• Experience with cloud platforms (AWS, GCP, Azure), containerization (Docker, Kubernetes), and MLOps tools (MLflow, Weights & Biases, or similar).
• Strong mathematical foundation in statistics, linear algebra, and optimization.
• Proven experience with predictive modeling, segmentation algorithms, and forecasting techniques.
• Ability to work with complex demographic and behavioral datasets and optimize model performance for production deployment.
• Excellent written and verbal communication skills with ability to explain complex technical concepts to business stakeholders and contribute to technical documentation and knowledge sharing.
Preferred:
• Experience with predictive analytics, consumer behavior modeling, or demographic analysis.
• Background in market research, business intelligence, or customer analytics.
Company:
𝐀𝐛𝐨𝐮𝐭 𝐎𝐜𝐜𝐮𝐥𝐲𝐭𝐢𝐜𝐬 Occulytics provides AI-powered solutions built exclusively for senior living sales and marketing. Founded in 2024, the company is headquartered in Chicago, USA, with a team of 2-10 employees. The company is currently Early Stage.

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