Junior Deep Learning Engineer (AI/ML)

We believe the best ideas come from diverse perspectives and a shared passion for innovation.

What You Will Do

- Develop and code complex neural network architectures using Python and modern frameworks like TensorFlow, PyTorch, or Keras.

- Engage in the full model development lifecycle—from data preprocessing and feature engineering to training, validation, and performance testing.

- Collaborate closely with data scientists and senior software engineers to integrate your AI models into larger, scalable applications.

- Systematically test model performance, debug challenging issues, and implement optimizations for enhanced accuracy and computational speed.

- Maintain and version control deep learning frameworks and model architectures using best practices and tools like Git.

- Thoroughly document your model designs, training procedures, and experimental results to ensure reproducibility and facilitate knowledge sharing within the team.

- Support the deployment and maintenance of AI applications into production environments (potentially leveraging cloud platforms like AWS or GCP).

- Stay current with the latest academic research and advancements in the deep learning field to propose and implement novel solutions to complex problems.

What You Bring

- A Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field from an accredited US institution.

- Strong foundational knowledge of deep learning concepts and various neural network architectures (e.g., CNNs, RNNs, Transformers).

- Demonstrated programming proficiency in Python and hands-on experience with at least one major deep learning library, such as TensorFlow or PyTorch.

- Exceptional problem-solving skills with a methodical approach to debugging complex code and diagnosing model performance issues.

- Excellent communication and teamwork abilities; you can articulate complex technical ideas clearly to both technical and non-technical colleagues.

- A genuine passion for learning and an eagerness to adapt in the rapidly evolving field of artificial intelligence.

- Foundational experience with core data science tools, including Scikit-learn, Pandas, and NumPy, is essential.

Bonus Points For

- Experience with cloud computing platforms (AWS, GCP, Azure) and their associated machine learning services.

- Knowledge of containerization technologies like Docker for creating reproducible and scalable environments.

- A portfolio on GitHub showcasing personal projects, contributions to open-source software, or participation in Kaggle competitions.

- Practical project experience in a specific AI domain, such as Natural Language Processing (NLP), Computer Vision, or Reinforcement Learning.