Founding Software Engineer, V2G VPP Platform

This is a remote position.

  Founding Software Engineer, V2G VPP Platform

Candidates must be authorized to work in the United States full-time. We are not able to sponsor applicants for work visas at this time.

Flexible location: work where you want to, either remotely across the U.S. or from our HQ in NYC 

About  us: We are backed by a recent $3.8 million investment from top silicon valley investors, we're on a mission to unlock the full potential of developers.

What We Do: Patterned Learning AI platform helps developers learn, grow, and excel by providing intelligent code assistance, identifying areas for improvement, and fostering a collaborative learning environment.

We have a flexible, work-from-home friendly, style of operation with an on site office that is optional. 

Benefits: We offer health benefits and generous PTO packages. 

Our office is dog friendly, has unlimited snacks & drinks, flexible working hours (get out for the pow day!), and lots of rock climbing & skateboarding. 

We also offer competitive:

Medical insurance

Vision insurance

Dental insurance

401(k).

At Patterned Learning AI, we are committed to being a fun, groundbreaking, and inclusive place to work.

We encourage all qualified candidates to apply regardless of race, color, ancestry, religion, national origin, sexual orientation, age, citizenship, marital or family status, disability, gender, gender identity or expression, pregnancy or caregiver status, veteran status, or any other legally protected status.

About the Role:

We are seeking a Founding Software Engineer for the V2G VPP (Vehicle-to-Grid Virtual Power Plant) Platform. This role involves leading and developing backend and frontend applications that orchestrate and execute complex energy system models. The ideal candidate will have experience in interfacing with energy markets as well as distributed energy devices in residential and commercial markets.

In this role, you will:

Provide technical vision and architecture for energy modeling applications that incorporate forecasting, optimization, and first-principles energy models.

Design, develop, and own software that models the performance of distributed energy systems and their financial value proposition, supporting various stages of thePatterned Learning AI  customer journey (from pre-sales to asset operation).

Work closely with product, key stakeholders, and external clients to understand and implement new requirements for the energy service platform.

Develop and design microservice interfaces and APIs that facilitate easy execution and use of energy modeling applications in production and development environments.

Troubleshoot issues related to algorithm code.

You should have:

7+ years of experience in developing software applications.

2+ years in a leadership role, either as an engineering manager or a technical lead responsible for software feature delivery.

Strong skills in software design, mathematics, and analytics, including experience in complex distributed systems environments.

Expertise in Python and TypeScript, and libraries for numerical and time-series modeling.

Familiarity with AWS services such as EC2, ELB, etc.

Proven track record in delivering software products/features throughout the software development lifecycle.

Excellent interpersonal and communication skills, with the ability to work effectively with cross-functional teams and stakeholders.

Collaborative attitude with a willingness to learn and adapt in the rapidly evolving energy industry.

Preferred Qualifications

Experience in developing software applications within the energy industry, preferably with distributed energy resources

Expert knowledge of distributed energy resources (PV, storage, EVs, EVSEs) and the revenue-generating methods for these resources in residential and commercial/industrial markets.

Familiarity with machine learning and optimization algorithms and concepts.

Applicants for this job

722 Applicants

23 Applicants in the past day

Applicant seniority level

48% Senior level applicants

42% Entry level applicants

2% Manager level applicants

2% Director level applicants

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