Senior AI Software Engineer (US)
- Compensation
- Not disclosed
- Location
- Hybrid (Boston, Massachusetts, US)
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 5+ years of professional software engineering experience, with substantial recent experience personally writing and shipping production code.
- Strong full-stack engineering fundamentals
- Demonstrated experience with knowledge graphs, ontologies, RAG, retrieval systems, graph databases, vector databases, or related technologies.
- Demonstrated experience designing, building, and maintaining production-quality, user-facing web applications.
- Experience making architectural decisions and evaluating technical tradeoffs across frontend, backend, data, infrastructure, and application integrations.
- Experience using AI tools for engineering across development, testing, deployment and observability.
- Strong understanding of UI/UX principles and product usability, with the ability to translate complex information into intuitive user experiences.
- Ability to rapidly prototype technical approaches and determine when to prioritize speed, simplicity, scalability, or long-term maintainability.
- Ability to work autonomously, manage competing priorities, and deliver results with minimal oversight.
- Strong communication and collaboration skills, including the ability to explain technical decisions to both technical and nontechnical stakeholders.
- Ability and enthusiasm to learn unfamiliar technical domains as needed; prior quantum computing experience is not required, but an interest in scientific applications is nice to have
- Comfortable and familiar with working in a startup environment
Full description
About the role
We are seeking a hands-on Senior Software Engineer to design and build a knowledge graph, ontology, and AI-enabled application related to quantum computing and design. You will work across the stack, with particular focus on the architecture and implementation of knowledge graphs, ontologies, graph and vector databases, RAG, on a small but mighty engineering team. Building, maintaining, and designing scalable graph retrieval based AI applications.
This position offers a high level of ownership and is well suited to an engineer who is comfortable working across the stack, solving ambiguous technical problems, and remaining deeply involved in product development. You will be encouraged to experiment boldly with emerging technologies, evaluate new approaches, and apply them thoughtfully to unlock breakthrough performance and deliver outsized impact.
This position is classified as exempt under applicable wage and hour laws and requires the regular exercise of independent judgment and discretion in leading the design, architecture, and development of software systems.
The pay range for this position $180,000 - $250,000 per year.
Please note: A handwritten cover letter is required. Candidates who meet this requirement will hear from us within 3 days.
What you'll do
- Design and build systems involving knowledge graphs, ontologies, graph and vector databases, RAG, and related AI-enabled application functionality.
- Design and implement scalable agentic pipelines, involving custom agent tools, loops, and evaluation techniques for agentic systems
- Design, architect, build, and continuously improve a modern, interactive web application, including selecting appropriate frameworks, and designing intuitive user interfaces..
- Familiarity with cloud platforms and system design
- Make architectural and product-related technical decisions, exercising independent judgment when evaluating tools, technologies, tradeoffs, and design patterns.
- Design and implement backend systems, data models, APIs, data pipelines, and integrations that support complex application functionality.
- Integrate AI-enabled features such as intelligent search, retrieval, recommendations, contextual assistance, and other emerging application capabilities.
- Collaborate with product, design, domain experts, and company leadership to translate business and user needs into practical technical solutions.
- Remain deeply involved in software development, personally writing, testing, debugging, and shipping production code.
- Contribute to engineering standards and best practices for software design, code quality, testing, documentation, security, and maintainability.
- Evaluate and adopt emerging technologies, including AI-assisted development tools, when they can meaningfully improve productivity, quality, or application performance.