Role Overview The AI Engineer Intern will develop and enhance Large Language Models (LLMs) to ... Familiarity with concepts like reinforcement learning, prompt engineering for LLMs, or building ...
Role Overview The AI Engineer Intern will develop and enhance Large Language Models (LLMs) to ... Familiarity with concepts like reinforcement learning, prompt engineering for LLMs, or building ...
Role Overview The AI Engineer Intern will develop and enhance Large Language Models (LLMs) to ... Familiarity with concepts like reinforcement learning, prompt engineering for LLMs, or building ...
Role Overview The AI Engineer Intern will develop and enhance Large Language Models (LLMs) to ... Familiarity with concepts like reinforcement learning, prompt engineering for LLMs, or building ...
Reinforcement Learning Intern information
What is a Reinforcement Learning Intern job?
A Reinforcement Learning (RL) Intern is responsible for researching, developing, and testing RL algorithms to solve complex problems. They typically work on tasks such as implementing reinforcement learning models, optimizing reward functions, and running experiments in simulated environments. Interns collaborate with researchers and engineers to refine models and improve the efficiency of RL systems. They usually have experience in machine learning, deep learning, and programming languages like Python. The role provides hands-on experience in applying RL techniques to real-world applications.
What are the key skills and qualifications needed to thrive in the Reinforcement Learning Intern position, and why are they important?
To thrive as a Reinforcement Learning Intern, you need strong knowledge of machine learning fundamentals, programming proficiency (usually in Python), and a background in mathematics or computer science, often demonstrated through academic coursework or relevant projects. Familiarity with popular machine learning libraries such as TensorFlow, PyTorch, and RL-specific frameworks like OpenAI Gym is typically expected. Effective problem-solving skills, attention to detail, and the ability to communicate technical findings clearly are valuable soft skills in this position. These capabilities enable interns to contribute meaningfully to research and development efforts, bridging theory and practical application in real-world reinforcement learning projects.
What kinds of projects or tasks can I expect to work on as a Reinforcement Learning Intern?
As a Reinforcement Learning Intern, you will typically work on tasks such as designing, implementing, and testing reinforcement learning algorithms, analyzing experimental results, and assisting with data preprocessing or environment development. You may also collaborate with senior researchers and engineers, participate in code reviews, and contribute to technical discussions or team meetings. In many organizations, interns are given the chance to work on real-world problems—ranging from optimizing robotic control systems to enhancing recommendation engines. This hands-on experience not only builds your technical expertise but also helps you develop valuable teamwork and communication skills, preparing you for a future career in AI or machine learning.
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Full-time
Posted 20 days ago
Job description
At Athena, we empower possibility through transformative delegation. Our mission is to build the world's premier delegation platform, combining the strengths of exceptional Executive Assistants with advanced AI technologies to save our clients millions of hours each year.
Role OverviewThe AI Engineer Intern will develop and enhance Large Language Models (LLMs) to improve task delegation workflows, instruction generation, and automate agentic processes. This role will also contribute to building and improving AI agentic systems that can reason through tasks, use tools, and support more reliable task execution across Athena workflows. You will play a crucial role in prototyping, developing, and deploying cutting-edge AI features that directly impact client productivity and efficiency.
ResponsibilitiesAssist in developing and fine-tuning large language models (LLMs) to better understand and generate instructions for complex tasks. You'll experiment with model parameters and training data to improve performance.
Build and integrate AI-driven features that improve task delegation workflows - for example, creating intelligent agents that break down client requests into actionable steps for our team.
Collaborate with senior AI engineers to prototype systems that use AI for agentic workflows, enabling the platform to automatically handle or delegate routine instructions.
Support the development of AI agentic components such as task planning, tool use, memory, prompt workflows, and orchestration logic to help agents operate more effectively in real-world scenarios.
Evaluate model outputs for accuracy and usefulness. Develop tests and metrics to assess how well the AI-generated instructions or recommendations are performing in real-world scenarios.
Help build lightweight evaluation harnesses and experiments to test agent behavior, workflow reliability, and the quality of AI-generated task execution.
Work cross-functionally with product and software engineers to implement AI solutions into the Athena platform. Communicate technical findings and iterate on solutions based on user feedback.
Currently pursuing (or recently completed) a degree in Computer Science, Stats, or related field, with coursework in machine learning or artificial intelligence.
Strong programming abilities in Python (and familiarity with ML libraries like TensorFlow, PyTorch). Comfortable with data structures, algorithms, and writing clean, efficient code.
Solid understanding of machine learning fundamentals and algorithms (classification, NLP, Deep Learning). Familiarity with concepts of training, fine-tuning, and evaluating models.
Knowledge of natural language processing techniques. Understanding how large language models work and experience using or implementing NLP models.
Interest in AI agentic systems, including areas such as prompt design, tool use, workflow orchestration, multi-step reasoning, or evaluation of LLM-based systems.
Ability to break down complex problems and experiment with creative AI solutions. Eagerness to learn new technologies and frameworks quickly.
Previous projects or internship experience involving machine learning or AI (especially work with LLMs or NLP projects).
Experience with ML ops or model deployment (e.g. using cloud AI services, Docker, REST APIs for model serving).
Familiarity with concepts like reinforcement learning, prompt engineering for LLMs, or building multi-agent systems.
Experience with data preprocessing pipelines, and working with datasets relevant to language models or automation tasks.
Exposure to agent evaluation, prompt pipelines, orchestration frameworks, or AI systems that interact with external tools or APIs is a plus.
Awareness of the latest trends and research in AI/ML (new model architectures, papers, etc.), showing a passion for staying up-to-date in the field.
You will be mentored by Athena's AI and software engineering experts. They will provide guidance, code reviews, and support as you work on challenging AI projects, ensuring you learn best practices in the field.
Expect to engage with cutting-edge AI technology. You'll contribute to pioneering projects (like improving AI-driven delegation) that could become core Athena offerings, giving you tangible achievements to highlight in your career.
Be part of a collaborative, forward-thinking team. Interns at Athena are included in all aspects of company life - from daily stand-ups to social outings - receiving the same respect and perks as full-timers.
This internship will enhance your practical AI skills and professional network. You'll leave with a deeper understanding of how AI can automate and improve real-world processes, and you may earn opportunities for future employment at Athena.com based on your performance.