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Remote Reinforcement Learning Jobs in Virginia (NOW HIRING)

Booz Allen Hamilton, Inc.

Agentic AI Engineer

Mclean, VA · On-site +1

$77K - $176K/yr

Experience with model fine-tuning, prompt tuning, domain adaptation, or reinforcement learning from ... Remote : If this position is listed as remote, there may still be occasions when you are required ...

Maximus

Sr Specialist, AI & Technical Upskilling

VA · On-site +1

... Remote Country United States Working time Full-time Description & Requirements Maximus' Learning ... assets, practical reinforcement, and measurable outcomes. This role is ideal for a learning ...

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Remote Reinforcement Learning information

What is a Remote Reinforcement Learning job?

A Remote Reinforcement Learning job involves developing and applying reinforcement learning algorithms while working from a location outside of a traditional office environment. Professionals in this field focus on creating systems where agents learn optimal behaviors through trial and error, often using feedback from their environment. These jobs typically require expertise in machine learning, programming, and mathematics, and are commonly found in industries like robotics, gaming, and autonomous systems. Working remotely allows researchers and engineers to collaborate with global teams using digital tools and platforms.

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

To thrive as a Remote Reinforcement Learning Engineer, you need a strong background in machine learning, statistics, and programming (especially Python), often supported by an advanced degree in computer science or a related field. Familiarity with frameworks such as TensorFlow, PyTorch, and RL-specific libraries like OpenAI Gym, along with experience using cloud computing platforms, is typically required. Excellent problem-solving skills, self-motivation, and effective remote communication help individuals excel in distributed teams. These skills ensure the successful design, implementation, and deployment of reinforcement learning solutions while collaborating efficiently in a remote work environment.

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

AspectRemote Reinforcement Learning
Required CredentialsMaster's or PhD in Computer Science, AI, or related fields; knowledge of RL algorithms
Work EnvironmentResearch-focused, experimental, often involves simulation and algorithm development
Employer & Industry UsageTech companies, research labs, AI startups focusing on autonomous systems
Common Search & Comparison IntentUnderstanding specialized AI roles, research focus, and technical skills

Remote Reinforcement Learning specialists focus on developing algorithms that enable machines to learn through trial and error in simulated or real environments. In contrast, Remote Machine Learning Engineers typically work on deploying and optimizing various machine learning models across applications. While both roles require strong programming skills and knowledge of AI, reinforcement learning emphasizes decision-making processes, whereas machine learning engineering covers a broader range of models and deployment strategies.

What are common challenges faced when working remotely in a Reinforcement Learning role and how can they be addressed?

Working remotely in a Reinforcement Learning role often involves overcoming communication barriers with cross-functional teams, managing large-scale experiments without on-site resources, and staying updated with rapidly evolving research. To address these challenges, it's important to establish regular check-ins with colleagues, utilize cloud-based platforms for experiment management, and participate in virtual seminars or journal clubs. Developing strong self-motivation and time management skills is also crucial to maintain productivity in a remote environment.
What are the most commonly searched types of Reinforcement Learning jobs in Virginia? The most popular types of Reinforcement Learning jobs in Virginia are:
What are popular job titles related to Remote Reinforcement Learning jobs in Virginia? For Remote Reinforcement Learning jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Remote Reinforcement Learning jobs in Virginia look for? The top searched job categories for Remote Reinforcement Learning jobs in Virginia are:
What cities in Virginia are hiring for Remote Reinforcement Learning jobs? Cities in Virginia with the most Remote Reinforcement Learning job openings:
Remote Reinforcement Learning jobs near you

Remote- AI & Financial Engineering Developer- ONLY W2

INFT Solutions Inc

Mclean, VA • On-site, Remote

Contractor

Posted yesterday

Job description

AI & Financial Engineering Developer

Location: McLean, Remote

Call notes:

This is a remote opportunity.
We use a variety of quantitative models to forecast mortgage defaults and prepayments in order to assess financial risk.
The goal is to leverage AI to assist users throughout the model execution lifecycle, including formatting inputs, interpreting data elements, and providing guidance during model execution.
Since we have different models for different mortgage products, the AI should be able to understand the specific model being executed and provide contextual assistance accordingly.
The AI should be capable of analyzing the underlying model code and business logic to explain what is happening during execution, identify potential issues, and help diagnose model outputs.
This role requires a unique combination of AI expertise and Financial Engineering knowledge, as the individual will be working at the intersection of both domains.
Development will primarily be done in Python.
Candidates should have experience with quantitative financial models, including prepayment models, credit risk models, valuation models, and risk models.
Similar to industry-standard models (e.g., Opus), all models go through required security and governance checks before being deployed. They are then hosted securely within internal endpoints for enterprise use.
Job Description: AI & Financial Engineering Developer
Location: McLean, Remote
Must Have Qualifications: 7+ years of software development experience, including experience with API development, AI application development, and programming languages such as Python, C++, and Scala. Candidates should have 1-3 years of financial industry experience, with exposure to large language models (LLMs) and agentic AI development is a strong plus. A degree is preferred but not required. Prior experience with Fannie or Freddie is a strong plus.
Position Overview
We are seeking a highly skilled AI & Financial Engineering Developer who combines deep expertise in artificial intelligence/machine learning with quantitative finance and financial engineering. This hybrid role is ideal for a technologist who thrives at the intersection of cutting-edge AI and complex financial systems.
Key Responsibilities
AI & Machine Learning
• Design, develop, and deploy machine learning models and AI-powered applications for financial use cases
• Build and optimize deep learning, NLP, and generative AI solutions
• Develop data pipelines and feature engineering frameworks for model training and inference
• Implement MLOps best practices including model versioning, monitoring, and continuous deployment
• Stay current with state-of-the-art AI research and evaluate applicability to financial domains
Financial Engineering
• Develop quantitative models for pricing, risk management, and portfolio optimization
• Implement algorithmic trading strategies and backtesting frameworks
• Build financial simulation engines (Monte Carlo, stochastic modeling, etc.)
• Design and develop derivatives pricing models and fixed-income analytics
• Create real-time market data processing and analytics systems
Software Development
• Write production-quality, scalable, and maintainable code
• Architect and build high-performance distributed systems
• Develop RESTful APIs and microservices for financial applications
• Implement robust testing, CI/CD pipelines, and documentation practices
• Collaborate with cross-functional teams including traders, quants, risk managers, and data engineers
Required Qualifications
• Education: Master’s or PhD in Computer Science, Financial Engineering, Quantitative Finance, Mathematics, Physics, or a related quantitative field
• Experience: 7+ years of professional software development experience, with at least 3 years in AI/ML and 2+ years in financial services or fintech
• Programming Languages: Expert proficiency in Python; strong skills in C++, Java, or Scala
• AI/ML Expertise: Hands-on experience with TensorFlow, PyTorch, scikit-learn, and large language models (LLMs)
• Financial Knowledge: Strong understanding of financial instruments (equities, fixed income, derivatives, structured products), market microstructure, and quantitative risk measures (VaR, Greeks, CVA)
• Mathematics: Advanced knowledge of stochastic calculus, linear algebra, probability theory, and numerical methods
• Data & Infrastructure: Experience with SQL/NoSQL databases, cloud platforms (AWS, Azure, or GCP), and big data technologies (Spark, Kafka)
Preferred Qualifications
• CFA, FRM, or equivalent financial certification
• Experience with reinforcement learning applied to trading or portfolio management
• Knowledge of blockchain/DeFi protocols and smart contract development
• Familiarity with regulatory frameworks (Basel III/IV, MiFID II, Dodd-Frank)
• Publications in AI/ML or quantitative finance journals
• Experience with real-time streaming systems and low-latency architectures
• Proficiency with LLM fine-tuning, RAG architectures, and AI agents for financial applications
Technical Stack (Preferred Experience)
Category Technologies
Languages Python, C++, Java, SQL, R
AI/ML PyTorch, TensorFlow, Hugging Face, LangChain, scikit-learn
Finance Libraries QuantLib, Zipline, Backtrader, pandas, NumPy
Cloud & Infra AWS/Azure/GCP, Docker, Kubernetes, Terraform
Data Spark, Kafka, Airflow, PostgreSQL, MongoDB, Redis
DevOps Git, CI/CD, MLflow, Weights & Biases