About the company
Superduper is the leading Web3 Entertainment Development company. Our focus: Development of consumer applications, infrastructure and IP led ventures. Our objective: To assist our partner brands to onboard the first billion users into digital assets. To date, they have raised over $50m in funding. We work closely with many of the world’s largest brands to bring new ideas to life. Supderduper is fast forwarding mainstream adoption of Web3 technologies.
Job Summary
Key Responsibilities
📍Build and optimize low‑latency, high‑throughput APIs that expose real‑time token mindshare and sentiment metrics to downstream clients. 📍Design and implement real‑time sentiment‑analysis and NLP pipelines for social feeds (Twitter, Reddit, Discord, Telegram, etc.), covering ingestion, tokenization, entity extraction, and sentiment scoring. 📍Develop and train ML models, starting with pre‑built services and advancing to custom transformer architectures, to continuously improve the accuracy and relevance of sentiment signals. 📍Collaborate with cross-functional teams (frontend, design, marketing, product) to roadmap and deliver new ML-driven insights and to design intuitive consumer-facing dashboards and alert systems that visualize real-time mindshare and sentiment metrics aligned with product goals. 📍Ensure data security and compliance, particularly around user‑generated content, API keys, and any PII in social media streams. 📍Maintain code and model quality: author clean, efficient, and maintainable code; implement comprehensive testing and debugging; and lead code reviews, share best practices, and mentor teammates.
Knowledge & Experience
đź“Ť5+ years in ML or data engineering roles, building production-grade NLP or sentiment systems. đź“ŤProven track record building low-latency, high-throughput data pipelines and APIs using Go, Python, or similar. đź“ŤHands-on NLP experience with both pre-built services (e.g., AWS Comprehend) and custom transformer models (Hugging Face, PyTorch, TensorFlow) with a strong grounding in evaluating NLP models using classification and ranking metrics, and experience running A/B or offline benchmarks. đź“ŤProficient with MLOps and training infrastructure (MLflow, Kubeflow, Airflow), including CI/CD, hyperparameter tuning, and model versioning. đź“ŤStrong social media data extraction and scraping skills at scale (Twitter v2, Reddit, Discord, Telegram, Scrapy, Playwright). đź“ŤExperience with real-time streaming systems (Kafka, RabbitMQ) and ingesting high-velocity data.
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