Remarcable

Full-stack Engineer

• Build and launch end-to-end features across Python/Django backends, Angular frontends, and SQL schemas
• Diagnose and resolve complex edge cases, race conditions, and performance bottlenecks across the stack
• Troubleshoot high-priority customer issues and runtime exceptions in live production environments
• Conduct peer code reviews to enforce quality, security, and architectural standards
• Build and maintain REST APIs, webhooks, email parsers, and OCR extraction pipelines
• Document architecture, data models, and workflows for team maintainability
• Use modern AI tools such as Cursor and Copilot to accelerate execution, automate testing, and learn new technologies
• Own features end to end, collaborate directly with customers, and contribute to product strategy

Full Stack Software Engineer

Account Executive

• Own a sales territory and create and close new business with contractors
• Learn customers’ purchasing, costing, tracking, and materials-management workflows and translate them into financial impact
• Build pipeline and relationships through targeted outreach, distributor relationships, local market research, and industry events
• Conduct discovery conversations with owners, purchasing managers, CFOs, and other stakeholders
• Deliver product demonstrations rooted in industry expertise
• Manage deals involving operations, accounting, and field stakeholders
• Negotiate with integrity and hand customers to Customer Success with a clear success plan
• Exceed monthly sales quotas
• Provide field intelligence to marketing and product teams
• Travel for customer visits and trade shows

Data Scientist

• Partner with Data Engineering to design the lakehouse’s semantic and analytical layers on AWS S3, Redshift, Athena, and Glue
• Translate ambiguous business questions into clear analyses and communicate findings to technical and non-technical stakeholders
• Analyze product, operations, and customer usage data to identify trends, anomalies, and opportunities
• Build models and heuristics powering Remarcable Intelligence, including smart search, recommendation engines, and forecasting
• Build AI/ML data infrastructure, including feature engineering, training datasets, and model evaluation, supporting SageMaker and Bedrock workflows
• Develop and maintain analytics for company metrics such as ARR, churn, NRR, product usage, and ROI
• Create customer-facing and internal reporting
• Design and validate product experiments, including A/B tests and other measurement methodologies
• Shape data architecture and deliver reporting, models, and insights that drive decisions