Athena is looking to enhance its delegation platform by improving task delegation workflows, instruction generation, and automating agentic processes through the development and enhancement of Large Language Models (LLMs).
Requirements
- 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.
- Ability to break down complex problems and experiment with creative AI solutions.
Responsibilities
- Assist in developing and fine-tuning large language models (LLMs) to better understand and generate instructions for complex tasks.
- 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.
- 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.
- 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.
Other
- Currently pursuing (or recently completed) a degree in Computer Science,Stats, or related field, with coursework in machine learning or artificial intelligence.
- 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.
