About Augment Code
Augment Code is the only AI coding assistant built for professional software engineers working in large, production‑grade codebases. Our Context Engine understands your entire repo, enabling developers to stay in flow while writing, reviewing, and understanding code. Backed by top‑tier investors and trusted by engineering teams at leading tech companies, Augment Code is redefining how modern software is built.
About the role
We’re looking for an Applied AI Engineer to help bring cutting-edge AI capabilities into the hands of developers. This is a hands-on engineering role at the intersection of product, AI, and systems — focused on implementing and integrating LLM-powered features that enhance developer experience and productivity.
You’ll collaborate with engineers, researchers, and designers to translate product needs into intuitive, reliable AI-powered experiences. You’ll also play a key role in shaping how AI is used across our stack — from prompt design to system integration — while staying up to date on emerging AI capabilities.
Examples of what you could do:
- Implement and integrate AI functionality into key product features
- Craft and iterate on prompts to improve LLM reliability and usefulness
- Build AI-powered flows that feel intuitive and responsive to developers
- Evaluate and test AI outputs to ensure performance and accuracy
- Work alongside engineers to deliver robust, production-grade code
- Stay current with LLM tools, APIs, and best practices
You will…
- Deliver reliable, high-quality AI-powered product experiences
- Translate product needs into technical AI implementations
- Tune and test prompts for real-world use cases and developer workflows
- Collaborate closely with engineers and researchers
- Contribute across frontend, backend, and integration layers
Qualifications:
- Strong coding skills in one or more of: Python, Go, Node.js, JavaScript, TypeScript, React, or Java
- Experience with API integrations and service-oriented architectures
- Familiarity with prompt engineering for LLMs (e.g. OpenAI, Claude, Gemini)
- Ability to evaluate and optimize AI outputs for reliability and quality
- Strong problem-solving instincts and attention to detail
- Collaborative mindset and eagerness to learn
Bonus Points:
- Experience building product features that incorporate LLMs
- Understanding of best practices for AI reliability and safety
- Background in frontend development or UX-oriented implementation
- Familiarity with cloud platforms (especially GCP)
- Basic understanding of LLM behavior, strengths, and limitations
Perks & Benefits
We invest in creating an environment where you can do your best work and enjoy showing up.
- Comprehensive medical, dental, and vision coverage
- Unlimited paid time off
- Mental health and family-forming support
- Commuter benefits, including pre-tax options and a monthly stipend
- Daily lunch, stocked kitchens, and regular in-office events
- Thoughtful office setups, plus workspace and tools stipends
- Onsite gym access and a pet-friendly HQ
- A collaborative, in-person culture built around connection and momentum
We will do everything we can within reason to make sure that your interview takes place in an environment that fairly and accurately assesses your skills. If you need assistance or accommodation, please contact your recruiter.
Augment Code is proud to be an Equal Employment Opportunity employer. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.
By applying for this job, the candidate acknowledges and agrees that any personal data contained in their application or supporting materials will be processed in accordance with Augment Code's Applicant Privacy Policy.
Pay Transparency Notice: The actual base salary within the stated range will be based on a combination of factors such as an individual's skills, experience level, educational background, and other relevant job-related considerations.
