X, The Moonshot Factory
Senior Machine Learning Engineer (Tapestry)
X, The Moonshot Factory, Mountain View, California, us, 94039
About The Team
Tapestry is Alphabet’s moonshot for the electric grid, working at the frontier where energy’s complexity meets AI’s potential. We were born at X, the innovation lab responsible for breakthrough technologies like Waymo, Verily and Google Brain.
To keep pace with humanity’s growing energy needs, the world needs a grid that is visible and understandable. We provide that clarity by building advanced AI-enabled analytical and planning tools that allow the entire energy ecosystem to plan smarter, move faster, and operate more efficiently—ensuring electricity remains reliable and affordable for everyone.
This is a global effort. Tapestry is proud to support partners in the U.S., U.K., Chile, New Zealand, Australia and Brazil as they build a cleaner, more resilient energy future. Joining Tapestry allows you to do the best work of your life as part of a multidisciplinary team of experts in AI, energy systems, software engineering and product design—all collaborating to reshape energy on a global scale. If you want to tackle problems that matter and build tools with real impact, we would love to meet you. Learn more about our team and our mission here.
About The Role We're looking for a Senior Machine Learning Engineer to join our team. In this role, you will be responsible for developing and deploying state of the art machine learning models to solve complex challenges that face today’s electric grid. You’ll have the opportunity to lead model building and development across diverse ML domains such as time series analysis, planning and reinforcement learning, multi modal information retrieval and reasoning and agentic AI. In this role, you will be responsible for end to end ML model training and deployment, working with and mentoring junior machine learning engineers, helping lead cross-functional collaborations and build the state of the art machine learning models to help power the grid of tomorrow.
How You Will Make 10x Impact
Design, train, and deploy machine learning models in production environments.
Lead the end to end development of ML systems for one or more of planning and reinforcement learning, forecasting and multimodal machine learning
Actively collaborate with leaders in product, engineering, and data science teams to address key business problems
Mentor junior engineers and scientists and contribute to the team’s technical growth.
Stay abreast of the latest advancements in machine learning
What You Should Have
Master’s Degree/Bachelor's Degree in Machine Learning, Computer Science, Statistics or equivalent practical experience.
6+ years of professional experience in machine learning model development and engineering.
Deep expertise in one or more of the following areas: planning and control, reinforcement learning, forecasting, multimodal machine learning and information retrieval
Strong programming skills in Python and experience with ML frameworks like PyTorch or TensorFlow.
Experience with building and deploying ML systems at scale, and a proven ability to perform applied ML research and develop the state of the art
Proven ability to work on cross-functional projects and deliver results in a fast-paced, collaborative environment
It’d Be Great If You Also Had These
PhD in Machine Learning, Computer Science, Statistics, or a related field
Experience with cloud platforms such as AWS, GCP, or Azure.
Familiarity with MLOps practices and tools.
A strong portfolio of projects demonstrating expertise in multiple ML domains.
The US base salary range for this full-time position is $166,000 - $244,000 + bonus + equity + benefits. 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.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.
Seniority level Mid-Senior level
Employment type Full-time
Job function Engineering and Information Technology
Industries Research
#J-18808-Ljbffr
To keep pace with humanity’s growing energy needs, the world needs a grid that is visible and understandable. We provide that clarity by building advanced AI-enabled analytical and planning tools that allow the entire energy ecosystem to plan smarter, move faster, and operate more efficiently—ensuring electricity remains reliable and affordable for everyone.
This is a global effort. Tapestry is proud to support partners in the U.S., U.K., Chile, New Zealand, Australia and Brazil as they build a cleaner, more resilient energy future. Joining Tapestry allows you to do the best work of your life as part of a multidisciplinary team of experts in AI, energy systems, software engineering and product design—all collaborating to reshape energy on a global scale. If you want to tackle problems that matter and build tools with real impact, we would love to meet you. Learn more about our team and our mission here.
About The Role We're looking for a Senior Machine Learning Engineer to join our team. In this role, you will be responsible for developing and deploying state of the art machine learning models to solve complex challenges that face today’s electric grid. You’ll have the opportunity to lead model building and development across diverse ML domains such as time series analysis, planning and reinforcement learning, multi modal information retrieval and reasoning and agentic AI. In this role, you will be responsible for end to end ML model training and deployment, working with and mentoring junior machine learning engineers, helping lead cross-functional collaborations and build the state of the art machine learning models to help power the grid of tomorrow.
How You Will Make 10x Impact
Design, train, and deploy machine learning models in production environments.
Lead the end to end development of ML systems for one or more of planning and reinforcement learning, forecasting and multimodal machine learning
Actively collaborate with leaders in product, engineering, and data science teams to address key business problems
Mentor junior engineers and scientists and contribute to the team’s technical growth.
Stay abreast of the latest advancements in machine learning
What You Should Have
Master’s Degree/Bachelor's Degree in Machine Learning, Computer Science, Statistics or equivalent practical experience.
6+ years of professional experience in machine learning model development and engineering.
Deep expertise in one or more of the following areas: planning and control, reinforcement learning, forecasting, multimodal machine learning and information retrieval
Strong programming skills in Python and experience with ML frameworks like PyTorch or TensorFlow.
Experience with building and deploying ML systems at scale, and a proven ability to perform applied ML research and develop the state of the art
Proven ability to work on cross-functional projects and deliver results in a fast-paced, collaborative environment
It’d Be Great If You Also Had These
PhD in Machine Learning, Computer Science, Statistics, or a related field
Experience with cloud platforms such as AWS, GCP, or Azure.
Familiarity with MLOps practices and tools.
A strong portfolio of projects demonstrating expertise in multiple ML domains.
The US base salary range for this full-time position is $166,000 - $244,000 + bonus + equity + benefits. 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.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.
Seniority level Mid-Senior level
Employment type Full-time
Job function Engineering and Information Technology
Industries Research
#J-18808-Ljbffr