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Deep Learning Developer Jobs in Florida (NOW HIRING)

Discover alpha in high-dimensional data with deep learning, time-series, and representation learning * Engineer scalable research pipelines from feature generation to distributed training and ...

AI Solutions Developer

Tampa, FL ยท On-site

$47.50 - $65.50/hr

Design build and optimize machine learning and deep learning models using PyTorch TensorFlow and ... Work closely with product engineering and business teams to translate strategic requirements into ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Design build and optimize machine learning and deep learning models using PyTorch TensorFlow and ... Work closely with product engineering and business teams to translate strategic requirements into ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

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Deep Learning Developer information

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

To thrive as a Deep Learning Developer, you need a strong background in computer science, mathematics, and proficiency in programming languages like Python, often supported by a degree in a related field. Familiarity with deep learning frameworks such as TensorFlow or PyTorch, and experience with cloud platforms or GPU acceleration, are commonly required technical skills. Analytical thinking, problem-solving abilities, and effective teamwork distinguish top performers in this role. These competencies are crucial for designing, training, and deploying advanced neural network models that address complex real-world problems.

What are some common challenges Deep Learning Developers face when deploying models to production environments?

Deep Learning Developers often encounter challenges such as optimizing model performance for real-time inference, managing resource constraints (like GPU/CPU availability), and ensuring model reproducibility across different environments. Additionally, integrating deep learning models into existing software systems and maintaining them over time can be complex, especially as data and requirements evolve. Collaborating closely with DevOps, data engineers, and QA teams is essential to address these challenges and ensure smooth deployment and ongoing reliability.

What are Deep Learning Developers?

Deep Learning Developers are specialized software engineers or data scientists who design, build, and implement artificial intelligence systems using deep learning techniques. They work with neural networks, large datasets, and various frameworks like TensorFlow or PyTorch to develop models for tasks such as image recognition, natural language processing, and autonomous systems. Their responsibilities include data preprocessing, model training, optimization, and deployment to solve complex problems that require advanced pattern recognition. Deep Learning Developers often collaborate with AI researchers, data engineers, and product teams to integrate intelligent features into applications.

Which 3 jobs will survive AI?

Deep Learning Developers are likely to continue to be in demand as AI advances because they design and improve AI models, requiring specialized skills in programming, mathematics, and data analysis. Other resilient roles include AI ethicists, who address ethical considerations, and AI system trainers, who curate and annotate data to improve AI performance. These jobs involve complex problem-solving and human oversight that are less easily automated.

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

AspectDeep Learning DeveloperMachine Learning Engineer
Required CredentialsBachelor's or Master's in CS, AI, or related; experience with neural networksBachelor's or Master's in CS, Data Science, or related; knowledge of algorithms
Work EnvironmentResearch labs, AI startups, tech companies focusing on neural networksData-driven companies, software firms, industries applying machine learning
Industry UsagePrimarily in AI research, neural network development, deep learning projectsBroader application including predictive modeling, data analysis, and ML systems

Deep Learning Developers specialize in neural networks and deep learning models, often working on AI research and complex algorithms. Machine Learning Engineers have a broader focus on developing, deploying, and maintaining machine learning models across various applications. While both roles require similar educational backgrounds, their focus areas and industry applications differ.

What cities in Florida are hiring for Deep Learning Developer jobs? Cities in Florida with the most Deep Learning Developer job openings:
Infographic showing various Deep Learning Developer job openings in Florida as of May 2026, with employment types broken down into 1% Locum Tenens, 31% Full Time, 62% Part Time, 5% Contract, and 1% Nights. Highlights an 80% Physical, 5% Hybrid, and 15% Remote job distribution.
Machine Learning Researcher, Options

Machine Learning Researcher, Options

Citadel

Miami, FL โ€ข On-site

Other

This job post hasย expired today.ย Applications are no longer accepted.


Job description

Job Description
Role Overview
At Citadel Securities, we are at a once-in-a-generation opportunity in the financial markets. Machine Learning Researchers on our Options team turn cutting-edge ideas and petabyte-scale data into bleeding edge models with direct trading impact. Our team of researchers iterate quickly, own decisions end-to-end, and operate with substantial autonomy, resources, and scope in a flat, no-bureaucracy environment.
Opportunities may be available from time to time in any location in which the business is based for suitable candidates. If you are interested in a career with Citadel, please share your details and we will contact you if there is a vacancy available.
Responsibilities
  • Own the full research lifecycle, from hypothesis, experiment design, model validation, risk/overfit controls, to deployment
  • Conduct cutting-edge research and development in machine learning (e.g. LLMs) at scale with a focus on industry leading techniques and their applications in quantitative finance
  • Ship models to production that move P&L in options markets-measured by clear, testable outcomes
  • Prototype test iterate fast The resources and support to take great ideas from concept to trading in a very short space of time
  • Discover alpha in high-dimensional data with deep learning, time-series, and representation learning
  • Engineer scalable research pipelines from feature generation to distributed training and backtesting
  • Develop trading intuition to translate insights into executable strategies
  • Leverage large scale compute and data (petabytes; large budgets) to run ambitious experiments and push the frontier
Skills and Preferred Qualifications
  • Masters or PhD degree in mathematics, statistics, physics, computer science, or another highly quantitative field
  • Advanced training and strong research track record in statistics, machine learning, AI, or another highly quantitative field
  • Deep knowledge of cutting edge large scale models and their training and design
  • Training techniques (pre-training, fine-tuning, RL, RLHF), and optimization methods
  • A results-oriented track record of having taken ML ideas from theory to measurable impact
  • Strong math fundamentals (linear algebra, probability, optimization) and mastery of regression/ML for large scale data
  • Fluency in Python (NumPy, PyTorch) and the ability to write clean, modular, performant code for large-scale experiments
  • Hands-on with modern machine learning (sequence models/transformers, representation learning, regularization, cross-validation, causal/robust inference) applied in practice
  • Bias to action & problem-solving demonstrated ability and comfort around owning decisions, iterating quickly, and simplifying complex problems to impactful solutions
  • Curiosity about markets and enthusiasm to learn microstructure, options dynamics, and volatility regimes on the job
About Citadel Securities
Citadel Securities is a technology-driven, next-generation global market maker. We provide institutional and retail investors with world-class liquidity, competitive pricing and seamless front-to-back execution in a broad array of financial products. Our teams of engineers, traders and researchers harness leading-edge quantitative research and the accelerating power of compute, machine learning and AI to power our analytics and tackle the market's and our clients' most critical challenges. Together, we are forging the future of capital markets. For more information, visit citadelsecurities.com .