At SES AI, base pay is one part of our total compensation package and is determined within a range. The base pay range for this role is between $90,000 and $133,000. SES AI considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate’s work experience, location, education/training, and skills.
What We Offer
Company paid Health and Dental insurance (ability to add dependents)
Global travel insurance for employees traveling while on business
Company sponsored retirement plan with 100% vesting and up to 5% match.
Life and AD&D Insurance
Employee Assistance Program
Six Paid Holidays, and one floating holiday per a quarter equivalent to 4 per calendar year
10 accrued vacation days per calendar year that increases with tenure.
Bonus + Equity, based on position and eligibility requirements
Note: SES AI benefit, compensation, and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
About SES AI
SES AI Corp. (NYSE: SES) is powering the future of global electric transportation on land and in the air with the world’s most advanced Li-Metal batteries. SES AI is the first battery company in the world to accelerate its pace of innovation by utilizing superintelligent AI across the spectrum of its business, from research and development; materials sourcing; cell design; engineering and manufacturing; to battery health and safety monitoring. Founded in 2012, SES AI is an Li-Metal battery developer and manufacturer headquartered in Boston and with operations in Singapore, Shanghai, and Seoul.
Learn more at SES.AI
Position Overview
We are seeking a strong Full-Stack Engineer to design and implement robust systems for handling large-scale datasets and delivering web-based AI model pipelines. The ideal candidate will have expertise in scalable backend systems, database architecture, and frontend development, as well as experience in deploying AI/ML workflows to end-users.
Location: Remote or Onsite in Woburn, MA
Key Responsibilities
Database and Backend Systems
- Architect, implement, and optimize scalable databases capable of handling large dataset storage, search, retrieval, and transfer operations.
- Develop robust APIs and backend solutions using Node.js, Python (Flask/FastAPI), or similar technologies.
- Design and implement secure and efficient data pipelines for AI model integration.
AI Model Product Pipeline Development
- Build end-to-end pipelines to deploy AI models as a user-facing web product.
- Integrate AI model workflows into web-based platforms, ensuring efficient model execution and result delivery.
- Collaborate with data scientists and AI researchers to align on deployment strategies and optimization.
Web-Based Platform Development
- Develop user-friendly, responsive web interfaces using frameworks such as React or Angular.
- Ensure seamless interaction between the frontend and backend, providing a smooth user experience for running AI models.
- Implement tools and features that allow users to visualize AI model outputs effectively.
System Design and Scalability
- Design and implement scalable system architectures for high-performance web applications.
- Optimize system performance to ensure reliability under high data and user load.
- Implement best practices for cloud-based deployment and containerized solutions (e.g., Docker, Kubernetes).
Required Qualifications
- Experience: 3–5 years of industrial experience in full-stack development and database design.
- Frontend Expertise: Proficiency with React.js, Angular, or similar frameworks.
- Backend Expertise: Strong experience with Node.js, Express, Flask, or FastAPI; working knowledge of relational and non-relational databases (e.g., MongoDB, Neo4j, SQL).
- Scalability: Proven ability to design and implement scalable systems with efficient data handling.
- AI/ML Experience: Familiarity with integrating AI/ML models into web platforms.
- Collaboration Skills: Excellent communication and teamwork skills, with experience working across multidisciplinary teams.
Preferred Qualifications
- Experience with cloud platforms (AWS, GCP, or Azure) and distributed computing.
- Familiarity with numerical computing and scientific data workflows.
- Knowledge of AI/ML frameworks (TensorFlow, PyTorch) and deployment practices.
- Understanding of security best practices for web and data systems.
