Entry-Level AI Research Intern – Python & Machine Learning

We are a fast-scaling U.S. technology lab that transforms fundamental research into deployable AI solutions. Our cross-functional teams span healthcare, finance, education, and gaming, which means your code may improve a cancer diagnosis model today and optimize e-sports analytics tomorrow. Curiosity fuels our roadmap; evidence drives our decisions.  

What You’ll Do  

- Assist senior scientists by designing small-scale experiments that test novel neural-network architectures.  

- Collect, clean, and label multimodal datasets—text, image, audio—ensuring ethical and statistical integrity.  

- Implement research pipelines in Python using TensorFlow, PyTorch, and Jupyter notebooks.  

- Monitor training runs, track metrics, and document anomalies for replication.  

- Digest peer-reviewed literature; summarize findings to spark hypothesis refinement.  

- Maintain meticulous research logs that link code commits, parameters, and results.  

- Visualize outcomes with Matplotlib, Seaborn, or Plotly to communicate trends to non-technical stakeholders.  

- Pair-program with staff engineers to convert promising prototypes into scalable microservices.  

- Present weekly progress in stand-ups—field questions, challenge assumptions, celebrate breakthroughs.  

- Champion inclusive team culture by giving and receiving constructive feedback.  

Tech Stack You’ll Touch  

- Python 3.12, Conda, Poetry.  

- TensorFlow 2.x and PyTorch 2.x for deep-learning workflows.  

- scikit-learn, Pandas, NumPy for classical ML and data wrangling.  

- Docker, Kubernetes, and GitHub Actions for reproducible environments.  

- Google Cloud Platform and AWS SageMaker for distributed training.  

- Weights & Biases, MLflow for experiment tracking.  

What You Bring  

- Bachelor’s degree in Computer Science, Data Science, or related quantitative field.  

- Solid command of Python fundamentals—list comprehensions, generators, OOP.  

- Familiarity with gradient-based learning, backpropagation, and loss functions.  

- Coursework or projects involving TensorFlow, PyTorch, or equivalent.  

- Ability to interpret confusion matrices, ROC curves, and statistical significance.  

- Foundation in linear algebra, probability, and calculus.  

- Clear, concise writing that translates complex methods into accessible summaries.  

- Adaptability when hypotheses fail—iterating energizes you.  

- Collaborative mindset; you thrive on pair debugging and knowledge sharing.  

- Eligibility to work in the United States.