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Salary: USD 200,000 - 250,000 / annual
Prolific is building human data infrastructure for AI development. The company connects researchers and enterprises with a global pool of participants to collect high-quality, ethically sourced human behavioral data and feedback—critical for training more accurate, nuanced, and aligned AI systems.
You will join a specialized product engineering team serving frontier model creators and enterprise AI application developers. Operating with high ownership and a product mindset at startup pace, you'll solve customer problems and capture business opportunities across Prolific's domains. You'll be part of a cross-functional team optimized for a single customer group, collaborating directly with account managers, customer success specialists, and customers to understand their problem spaces deeply.
As a full-stack engineer, you will work across the breadth of Prolific's codebase, translating business concepts into software models and delivering at high engineering standards. You'll ideate and build with autonomy, supported by a high-performing team. The role includes supporting production systems and responding to incidents when required. You'll have regular in-person collaboration with customers and the US team, as well as close collaboration with UK-based tech teams.
The position is hybrid based in the New York office, approximately 1–2 days per week on-site.
Key Technologies: Python (Django, FastAPI), TypeScript/JavaScript (Vue.js), SQL and NoSQL databases (PostgreSQL, MongoDB, DynamoDB), Google Cloud Platform and AWS, Kubernetes, CircleCI, GitHub Actions, Celery, EventBridge, and Datadog.
REQUIREMENTS:
- Over 4 years of experience in a product engineering role
- Ability to translate business concepts into software models
- Quick learning and adaptation across diverse technical domains
- Experience with both monolithic and distributed systems
- Strong communication and collaboration skills for direct customer interaction
- Good understanding of modern web applications and architecture design patterns
- Experience supporting applications in production environments
- Judgment to balance scrappy startup execution with scalable, reliable engineering
- Comfort with rapid iteration and responding to customer queries with urgency
- Experience with some of the technology stack: Python (Django, FastAPI), TypeScript/JavaScript (Vue.js), SQL/NoSQL databases (MongoDB, PostgreSQL), cloud deployment (GCP, Kubernetes, GitHub Actions, CircleCI), and observability tools (Datadog)
