AI Engineer

As an AI Engineer at Coastal Carbon, your main responsibilities will include collaborating with researchers to implement and scale proof-of-concept models, training large-scale foundation models, and developing AI systems that accurately understand the natural world. You will also build distributed training systems on high-performance computing clusters and tackle non-ML software issues related to data quality and job startup speed.

To qualify for the AI Engineer position at Coastal Carbon, you should have a Bachelor’s degree in computer science, engineering, or a related field along with at least 3 years of relevant work experience. Experience with deep learning frameworks such as PyTorch or TensorFlow and a willingness to dive into large ML codebases are also important.

While Coastal Carbon strongly prefers AI Engineers to work in-person in San Francisco, they are open to considering exceptional remote candidates. The goal is to find the right talent who can significantly contribute to the team's mission, regardless of their location.

An AI Engineer at Coastal Carbon should be proficient in deep learning and transformer models, with experience in frameworks like PyTorch or TensorFlow. Familiarity with cloud platforms such as AWS, and scripting languages like Python or Bash, as well as knowledge of containerization technologies like Docker, will be advantageous.

Coastal Carbon values team players who are adaptable and willing to take on various tasks to support the team. An AI Engineer should also be curious and passionate about using AI to tackle real-world problems in nature conservation and climate tech.

When answering this question, it’s important to detail any relevant projects you’ve worked on where you had to scale machine learning models. Discuss the frameworks you used, the challenges you faced, and how you tackled them. Highlight your ability to implement the latest state-of-the-art methods.

In your response, mention specific strategies you’ve used to maintain high data quality, such as data cleansing techniques, validation processes, and error checking methods. Highlight your understanding of how key data quality impacts model performance.

Speak about any experiences you've had setting up or managing distributed training on high-performance computing clusters. Discuss the tools and technologies you've worked with and your approach to ensuring smooth operations for large ML training jobs.

When addressing this question, choose a specific project or problem you encountered, explaining how you approached it, the methods you used, and the outcome. This showcases your problem-solving skills and adaptability in challenging situations.

Be specific about your experiences with frameworks like PyTorch or TensorFlow. Discuss projects where you've utilized these frameworks, focusing on your contributions and familiarity with their functionalities, particularly in training and fine-tuning models.

Express how you engage with the AI and ML community, such as participating in conferences, following influential researchers, or contributing to open-source projects. Highlight any online courses or literature that have recently influenced your work.

Identify key challenges such as ethical considerations, data privacy, or model bias. Share your insights on how the AI community can address these issues and any personal initiatives you’ve taken to contribute to solutions.

Explain your systematic approach to debugging. Discuss how you identify the root cause, the tools you used, and how you tested various solutions. This demonstrates your analytical skills and problem-solving capabilities.

Detail your experiences deploying models on cloud platforms like AWS, GCP, or Azure. Explain how you configured services for scalability and reliability, and the advantages you found in cloud computing for machine learning applications.

Express the importance of teamwork in AI projects. Share examples of how you have collaborated with researchers or engineers, highlighting the value of diverse perspectives and collective problem-solving in achieving innovative solutions.