Machine Learning Engineering Intern

Mountain View, CA

How you will make 10x impact:

• Explore the potential of leading edge reasoning models to solve large-scale planning and optimization problems in collaboration with human experts.
• Leverage neural/foundation modeling techniques to build models of and generative optimization algorithms for a wide range of real-world modeling tasks
• Translate your expertise in neural modeling to the rest of the team and apply it to solve real-world engineering problems related to climate sustainability and the carbon economy
• Collaborate with X project team to experiment and build novel insights to finance, risk management, portfolio management, and derivatives valuation problems
• Rapidly develop prototypes of artificial intelligence, machine learning models applied to sustainability and finance problems
• Apply the latest in data science techniques to build and deploy data pipelines and exploratory data analysis on large-scale datasets
• Develop value chain maps, evaluate costs and revenues to assess and recommend areas in which to de-risk the project
• Work cross-functionally with experts across, X, Alphabet, and academia
• Engage with the intern community during the program (attending tech talks, colloquium, and learning moonshot thinking), and most importantly HAVE FUN!

This project aims to push the limits of science and modeling as we know them and to prove how ML can radically accelerate our understanding of the world

• Location: X's headquarters in Mountain View, CA
• Start Date(s): Year-round rolling basis
• Duration: a flexible 4 mo. to 1 year program based on project team needs and your availability

Throughout your AI Residency you can expect:

• To be embedded into one of our confidential or public X projects
• To get paid competitively and receive benefits
• To be a part of a lively community of AI and ML Residents
• To attend tech-talks with AI leaders from across X

What you should have:

• Masters or PhD student in a STEM field (financial engineering, risk management, operations research, math/statistics or other quantitative field)
• Strong knowledge of financial and probabilistic theory, as applied to financial markets
• 5 years experience with simulations of real-world phenomena (e.g., physical, biological, ecological, sociological)
• Understanding of decarbonization and carbon removal technologies, as well as carbon markets
• Fluent in Python, C , SQL, databases, scripting, Matlab/R, SAS, and data visualization
• Strong knowledge of statistical or machine learning modeling techniques, including Large Language Models.
• Experience in using distributed computing resources, such as GCP, AWS, Azure or HPC clusters
• Very strong strategy and analytical skills. Ability to gather and analyze large quantities of information, and turn them into insightful business recommendations
• Good at problem stating and problem solving, and turning ambiguous problem spaces into radical solutions. Must be able to break down problems independently
• Can do attitude, willing to learn new tools and content to become an effective thought partner with project teams
• Excellent communication skills i.e experience writing strategy docs, experience delivering presentations, and a general openness to liaise with senior external partners
• Motivated by making the world a better place through technology, and wanting to be part of a team that makes this happen.

It’d be great if you also had these:

• Degree in a science, math, or engineering field
• An ability to thrive on ambiguity, setting own goals and effectively delivering them in a fast-changing environment
• Adept at influencing without authority, working cross-functionally with different teams in order to drive decision making

Additional public information:

https://www.wired.com/video/watch/astro-teller-captain-of-moonshots-at-x-speaks-at-wired25

https://www.bloomberg.com/news/videos/2019-10-10/alphabet-x-s-astro-teller-on-bloomberg-studio-1-0-video

The US base salary range for this position is $109,000 - $150,000 benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your location during the hiring process.