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Data Scientist Deep Learning Jobs (NOW HIRING)

TENEX.AI

Senior Data Scientist

San Jose, CA · On-site

... deep learning, and security-specific AI to drive platform innovation. • Explore novel statistical methods and machine learning techniques to tackle emerging and sophisticated cyber threats. • ...

tenex

Senior Data Scientist

San Jose, CA · On-site

Stay up-to-date on state-of-the-art research in data science, deep learning, and security-specific AI to drive platform innovation. * Explore novel statistical methods and machine learning techniques ...

Compunnel

Senior Data Scientist

Raleigh, NC · On-site

Key Responsibilities • Apply machine learning algorithms, including deep learning, gradient boosting, and random forests, to solve complex data science problems. • Design, implement, evaluate ...

AIToolboard

Data Scientist Level 3

Suitland, MD · On-site

$94K - $198K/yr

Apply deep learning and natural language processing (NLP) techniques to develop sophisticated data models and algorithms.\* Leadership and Mentorship: Lead and mentor junior data scientists ...

Apex Informatics

AI Data Scientist

... on deep learning and LLM approaches • Stay abreast of leading-edge technologies in machine ... or data science: data analysis, algorithm design, model architecture specification, machine ...

Augment

Principal Scientist, Data

Houston, TX · On-site

$80 - $83/hr

Data Scientist - AI/ML Focus Worksite: Onsite Monday-Thursday (Mandatory) - Houston, TX Must-Have ... Design, train, fine-tune, and evaluate machine learning and deep learning models--including LLMs ...

JS Consulting

Data Scientist

Washington, DC · On-site

Work or educational background in one or more of the following areas: machine learning, computational linguistics, deep learning, ratification intelligence, data science and/or data analytic ...

Cypress HCM

Data Scientist

San Jose, CA

$105.63/hr

Data Scientist Job Details * Data Scientist (Contract) * Location: San Jose CA 95110 (Hybrid ... The ideal candidate will have deep technical skills, a passion for delivering compelling customer ...

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Data Scientist Deep Learning information

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$37.5K

$122.7K

$196.5K

How much do data scientist deep learning jobs pay per year?

As of Sep 13, 2026, the average yearly pay for data scientist deep learning in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is a data scientist deep learning?

Data Scientist Deep Learning roles focus on designing, building, and implementing deep learning models to solve complex problems using large datasets. These professionals apply neural networks and advanced machine learning techniques to tasks such as image recognition, natural language processing, and predictive analytics. They work with programming languages like Python, use frameworks such as TensorFlow or PyTorch, and often collaborate with cross-functional teams to turn data insights into actionable solutions. Strong mathematical, statistical, and programming skills are essential for success in this role.

What are the key skills and qualifications needed to thrive as a data scientist deep learning?

To excel as a Data Scientist specializing in Deep Learning, you need a strong background in mathematics, statistics, and programming (often Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with deep learning frameworks such as TensorFlow or PyTorch, as well as experience in handling large datasets and cloud platforms, is essential, and certifications in machine learning can be advantageous. Analytical thinking, problem-solving, and effective communication are crucial soft skills for interpreting data results and collaborating with cross-functional teams. These skills and qualities are vital for building advanced AI models, deriving actionable insights, and driving innovation in data-driven organizations.

How do data scientist deep learning professionals typically collaborate with other teams in a tech organization?

Data Scientist Deep Learning professionals frequently work cross-functionally, partnering with data engineers to prepare and optimize data pipelines, collaborating with machine learning engineers to deploy and scale models, and communicating findings to product managers and stakeholders in accessible terms. This collaborative environment ensures that deep learning solutions are both technically robust and aligned with business goals. Regular meetings and agile workflows help facilitate smooth communication and integration of deep learning models into production systems.

What is the difference between Data Scientist Deep Learning vs Data Scientist Machine Learning?

AspectData Scientist Deep LearningData Scientist Machine Learning
Required CredentialsBachelor's/Master's in CS, Data Science, or related; experience with neural networksBachelor's/Master's in CS, Data Science, or related; knowledge of algorithms
Work EnvironmentResearch, AI-focused projects, neural network developmentData analysis, predictive modeling, algorithm development
Industry UsageAI, computer vision, NLP, speech recognitionFinance, marketing, healthcare, general analytics
Common Search/ComparisonYesYes

Data Scientist Deep Learning specializes in neural networks and AI-driven models, often working on complex tasks like image recognition and NLP. Data Scientist Machine Learning covers a broader range of algorithms and applications, including predictive analytics and traditional machine learning models. Both roles require strong programming skills and statistical knowledge, but Deep Learning roles focus more on neural network frameworks and AI-specific tools.

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What are the most commonly searched types of Data Scientist Deep Learning jobs?

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Data Scientist Deep Learning jobs near you
Infographic showing various Data Scientist Deep Learning job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 84% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Automation Engineer - Computer Vision / Deep Learning Data Scientist

Thousand Oaks, CA • On-site

Milestone Technologies
See more jobs at Milestone Technologiesexternal

Contractor

Posted 9 days ago

Job description

Senior Data Scientist / ML Engineer - Computer Vision
Position Overview
We are seeking a Senior Data Scientist / ML Engineer specializing in Computer Vision to support the development, training, optimization, and deployment of machine vision solutions for automated visual inspection in a manufacturing environment.
This role will be a key contributor to an internally developed computer vision platform supporting production packaging operations. Unlike commercial off-the-shelf machine vision solutions provided by external vendors, this system has been custom designed and developed in-house to support specific manufacturing requirements and long-term digital transformation initiatives.
The successful candidate will help scale and mature an existing deployment currently operating on a packaging line equipped with approximately 40 cameras and will contribute to future expansion across additional manufacturing lines and facilities. The position offers the opportunity to work on a highly visible initiative that represents one of the organization's first large-scale implementations of AI-powered visual inspection technology.
This is an excellent opportunity for a hands-on machine learning professional who enjoys solving real-world industrial challenges and helping organizations adopt emerging technologies in operational environments.
Key Responsibilities
Computer Vision & Machine Learning
  • Design, develop, train, validate, and optimize deep learning models for industrial image inspection and anomaly detection.
  • Support the full machine learning lifecycle from dataset review through production-ready model evaluation.
  • Analyze image datasets and identify opportunities to improve data quality, coverage, and labeling consistency.
  • Implement image preprocessing, augmentation, ROI selection, masking, and feature engineering techniques.
  • Develop anomaly detection workflows using supervised and unsupervised learning approaches.
  • Evaluate model performance and optimize confidence thresholds to minimize false positives and false negatives in production environments.
  • Troubleshoot model performance issues and recommend corrective actions.
Operational Technology (OT) Collaboration
  • Work collaboratively with manufacturing, engineering, automation, and operational technology teams.
  • Develop an understanding of manufacturing workflows and production line operations.
  • Demonstrate familiarity with Operational Technology (OT) environments and the unique requirements associated with industrial systems.
  • Collaborate effectively with stakeholders across both IT and OT functions to ensure successful deployment and adoption of machine vision solutions.
Data & Model Lifecycle Management
  • Support image dataset development, curation, governance, and traceability.
  • Establish robust train, validation, and test methodologies.
  • Identify and mitigate data leakage and dataset bias risks.
  • Maintain reproducible training procedures and version-controlled model development practices.
  • Assist in ongoing performance monitoring and model improvement efforts.
Cloud & Infrastructure Support
  • Leverage AWS services including SageMaker, S3, CloudWatch, and EC2 to support model development and operational monitoring.
  • Work alongside engineering teams to support deployment and maintenance of machine learning workloads.
Documentation & Knowledge Transfer
  • Create and maintain detailed technical documentation covering datasets, model configurations, training runs, validation results, and deployment procedures.
  • Document model decision-making processes and provide traceability for future audits and troubleshooting.
  • Support ongoing knowledge transfer and cross-functional training activities.
Recommended Experience
Required
  • 12+ years of professional experience in Data Science, Machine Learning, Artificial Intelligence, or related fields.
  • 3+ years of hands-on experience developing Computer Vision and Deep Learning solutions.
  • Proven experience delivering image-based machine learning solutions through the complete lifecycle:
    • Dataset assessment
    • Data preparation
    • Model training
    • Hyperparameter tuning
    • Model evaluation
    • Performance optimization
  • Experience troubleshooting machine learning models and interpreting training, validation, and inference results.
  • Experience working directly with business and technical stakeholders to solve operational challenges.
Preferred
  • Experience with industrial machine vision or automated visual inspection systems.
  • Experience developing anomaly detection solutions.
  • Familiarity with manufacturing, pharmaceutical, life sciences, or regulated production environments.
  • Familiarity with Operational Technology (OT) environments and industrial systems.
  • Understanding of GxP-controlled environments and validation processes.
Not Required
  • Prior SmartVision experience.
  • SCADA programming experience.
  • PLC programming experience.
  • AWS administration experience.

Structured onboarding, shadowing, and knowledge transfer will be provided to support ramp-up.
Required Technical Skills
Programming & AI/ML
  • Strong Python development skills.
  • Strong experience with PyTorch or equivalent deep learning frameworks.
  • Experience utilizing modern AI-assisted development tools, including GitHub Copilot, Codex-based development workflows, or similar code-generation and productivity platforms.
  • Ability to rapidly prototype, test, and iterate on machine learning solutions using AI-enhanced development practices.
Computer Vision
  • Image classification
  • Object detection
  • Segmentation
  • Feature extraction
  • Visual anomaly detection
  • Image preprocessing and augmentation
  • ROI selection and masking
Model Development
  • Hyperparameter tuning
  • Threshold optimization
  • Sensitivity analysis
  • Model explainability and troubleshooting
  • Model evaluation using:
    • Precision
    • Recall
    • F1 Score
    • Confusion matrices
    • False Positive Analysis
    • False Negative Analysis
AWS Knowledge
  • Amazon SageMaker
  • Amazon S3
  • Amazon CloudWatch
  • EC2 familiarity preferred
Soft Skills:
  • Strong accountability and ownership of deliverables.
  • Excellent communication skills with both technical and non-technical audiences.
  • Patience and adaptability while working through evolving datasets, new technologies, and operational constraints.
  • High availability and responsiveness when supporting business-critical initiatives.
  • Strong collaboration and relationship-building skills.
  • Curiosity and willingness to learn a custom-developed platform and manufacturing processes.
  • Ability to work independently while maintaining alignment with project stakeholders.
Work Location
  • Remote work arrangement is acceptable.
  • Periodic travel to manufacturing facilities and project locations should be expected for onboarding, shadowing, knowledge transfer, testing, and deployment support.

The estimated pay range for this position is USD $75.00/Hr - USD $80.00/Hr. Exact compensation and offers of employment are dependent on job-related knowledge, skills, experience, licenses or certifications, and location. We also offer comprehensive benefits. The Talent Acquisition Partner can share more details about compensation or benefits for the role during the interview process.

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