Superduper is looking for a Machine Learning Engineer to architect the real-time platform that transforms noisy social feeds into clear token insights. You will power a fast, intuitive product by ingesting data from every channel, building advanced NLP models, and delivering sub-second API responses.

What You'll Do

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, 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 to roadmap and deliver new ML-driven insights and to design intuitive consumer-facing dashboards and alert systems.
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.

What We're Looking For

5+ years in ML or data engineering roles, building production-grade NLP or sentiment systems.
Proven track record building low-latency, high-throughput data pipelines and APIs using Go, Python, or similar.
Hands-on NLP experience with both pre-built services and custom transformer models.
Strong grounding in evaluating NLP models using classification and ranking metrics, and experience running A/B or offline benchmarks.
Proficient with MLOps and training infrastructure, including CI/CD, hyperparameter tuning, and model versioning.
Strong social media data extraction and scraping skills at scale.
Experience with real-time streaming systems and ingesting high-velocity data.
Deep data-engineering expertise across Postgres, Redis, InfluxDB, and ClickHouse.
Experience deploying microservices in production using Docker and Kubernetes.
Skilled in setting up observability and alerting pipelines, including model drift detection.
Experience with real-time ML inference and model serving frameworks for low-latency applications.
Experience designing feedback loops, active learning, or user-in-the-loop systems to continuously improve model relevance.
Experience with Git-based workflows and integrating model training into CI/CD pipeline.

Nice to Have

Experience fine-tuning large-scale transformer models and prompt-engineering for sentiment tasks.
Background building active-learning and annotation pipelines to bootstrap training data.
Familiarity with semantic search or vector databases for topic modeling and similarity queries.
Familiarity with crypto markets, order books, and risk-management frameworks.
Familiarity with anomaly-detection methods for streaming text and time-series data.
Experience developing EVM smart contracts with Solidity and modern toolchains.
Experience with real-time subscription frameworks or gRPC streaming for live data updates.

Technical Stack

Languages: Go, Python, Solidity
ML & NLP: AWS Comprehend, Hugging Face, PyTorch, TensorFlow, BERT, GPT
MLOps & Pipelines: MLflow, Kubeflow, Airflow, Scrapy, Playwright, Kafka, RabbitMQ
Infrastructure & Data: Postgres, Redis, InfluxDB, ClickHouse, Docker, Kubernetes, Elasticsearch, FAISS, Pinecone
Serving & Observability: TorchServe, Triton, BentoML, Prometheus, Grafana
Tools & Protocols: Git, Foundry, Hardhat, GraphQL, WebSockets, gRPC

Team & Environment

You will collaborate with cross-functional teams including frontend, design, marketing, and product. We are a high-performance team in a fast-paced, dynamic, and innovation-focused environment. We embrace a scrappy start-up culture, see ambiguity as opportunity, and treat speed and agility as key competitive advantages.