Machine Learning Engineer, Applied Science and Technology, Environmental AI

Key Skills

AWS, Azure, Computer Vision (CV), Data science techniques, Deep Learning, Docker, Google Cloud Platform (GCP), JAX framework, Machine learning techniques, Natural Language Processing (NLP), Python Programming, PyTorch, spaCy

Job Description

We are a newly formed lean and fast moving AI team exclusively dedicated to Environmental AI. We believe that AI can help solve some of the biggest problems the world faces. We strongly believe in open and transparent AI (as does the broader organization). We highly value the machine learning research, academic, and engineering communities and we believe in fostering meaningful collaborations between them. We work closely with the many talented research scientists at AI2 (eg. Aristo, PRIOR) and we consider ourselves fortunate to have that opportunity. We publish when we can, but we spend the vast majority of our time on engineering and shipping AI products to users. The models we ship run at global scale in real time and must be performant. We all live in Seattle and we enjoy working together in person, or meeting over coffee. We teach and learn from one another constantly and we help each-other grow. We have a leadership team that strongly believes in us and believes that AI can be a powerful force for good. We want to get great things done and we want to move quickly, because there is an urgency to the environmental threats that our planet is facing. Check out the following to learn more about our work, team culture, code, papers, talks, and ideas about what we might build together.

Your Next Challenge:

The essential functions include, but are not limited to the following:

• Expertise in both NLP (including LLMs) and CV. If you are not comfortable working with state of the art deep learning (both NLP and CV), this is not a good fit. By comfortable we mean the ability to read current research and then implement those ideas and improve on them or create new bespoke architectures from scratch.
• Ability to read, write, debug, and optimize ML/AI code.
• Ability to read and apply current research in AI
• Deep understanding of the entire ML software development life cycle and how to advance it
• Strong collaboration and teamwork skills

What You’ll Need:

• Minimum of a Bachelor’s degree in CS, EE/Applied Mathematics/Statistics/ML/NLP, or relevant engineering experience
• Experience with statistics and time series analysis
• Expertise in Python
• Familiarity working with cloud services (GCP, AWS, or Azure)
• Experience with containerization (Docker)
• Experience with the complete model development cycle, including data set construction, training, tuning, evaluation, performance profiling and optimization, and monitoring.

Preferred:

• Published papers in ML/AI (NLP or CV)
• Open source contributions
• Evidence of work on environmental cause
• Highly proactive and self-motivated
• Experience with LLM assisted engineering
• Experience with satellite imagery and/or AIS/GPS Data

Physical Demands and Work Environment:

The physical demands described here are representative of those that must be met by a team member to successfully perform the essential functions of this position. Reasonable accommodations may be made to enable individuals with disabilities to perform the functions.

• Must be able to remain in a stationary position for long periods of time.
• The ability to communicate information and ideas so others will understand. Must be able to exchange accurate information in these situations.
• The ability to observe details at close range.
• Can work under deadlines.

A Little More About AI2:

The Allen Institute for Artificial Intelligence is a non-profit research institute in Seattle founded by Paul Allen. The core mission of AI2 is to contribute to humanity through high-impact research in artificial intelligence.

In addition to AI2’s core mission, we also aim to contribute to humanity through our treatment of each member of the AI2 Team. Some highlights are:

Minimum of a Bachelor’s degree in CS, EE/Applied Mathematics/Statistics/ML/NLP, or relevant engineering experience

Experience with statistics and time series analysis

Expertise in Python

Familiarity working with cloud services (GCP, AWS, or Azure)

Experience with containerization (Docker)

Experience with the complete model development cycle, including data set construction, training, tuning, evaluation