Machine Learning Engineer (London)

Superduper

London

5 days ago

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Who We Are We are a cutting-edge DeFi automation and social trading platform revolutionizing how users, builders, and protocols interact with crypto. By combining seamless automation, gamification, and social engagement, we empower traders to discover, execute, and share innovative trading strategies across multiple chains. With a focus on user-driven growth and simplified blockchain workflows, we are building the future of decentralized financewhere trading is smarter, faster, and more connected. Join us in shaping the next generation of crypto innovation.

The Role As a Machine Learning Engineer, you will play a key role in architecting our real-time mindshare platform that turns noisy social feeds into crystal-clear token insights. Your work will power a product so fast and intuitive, ingesting data from every channel, powering advanced NLP models, and delivering sub-second API responses that will deliver a trading experience that end users will only describe as magic. This role offers a unique opportunity to collaborate with a high-performance team in developing a cutting-edge product that will redefine the DeFi ecosystem and advance our mission to transform the industry. You will be joining a stealth-mode initiative supported by a well-funded company, allowing you to innovate in a fast-paced, dynamic environment with the resources and support needed to push the boundaries of technology. Your work will directly contribute to creating a product that will have a profound impact on the world of DeFi, underpinned by the financial strength and strategic direction of our organisation. Key Responsibilities

Build and optimize lowlatency, highthroughput APIs that expose realtime token mindshare and sentiment metrics to downstream clients.
Design and implement realtime sentimentanalysis 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 prebuilt 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 usergenerated 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

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 (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.
Proficient with MLOps and training infrastructure (MLflow, Kubeflow, Airflow), including CI/CD, hyperparameter tuning, and model versioning.
Strong social media data extraction and scraping skills at scale (Twitter v2, Reddit, Discord, Telegram, Scrapy, Playwright).
Experience with real-time streaming systems (Kafka, RabbitMQ) and ingesting high-velocity data.
Deep data-engineering expertise across Postgres, Redis, InfluxDB, and ClickHouseschema design, indexing, and caching for sub-second reads.
Experience deploying microservices in production using Docker and Kubernetes.
Skilled in setting up observability and alerting pipelines (Prometheus, Grafana), including model drift detection.
Experience with real-time ML inference and model serving frameworks (e.g., TorchServe, Triton, BentoML) 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

Ideal Candidate Profile

A creative problem-solver who is eager to innovate and push boundaries in the DeFi space.
Deep expertise in data engineering and ML pipelines, with strong understanding of sentiment analysis, topic modeling, and production model deployment
Thrives in scrappy start-up environments, seeing ambiguity as an opportunity rather than an obstacle.
Comfortable taking ownership of complex problems and transforming them into user-friendly solutions.
Skilled at communicating technical ML concepts and results clearly and concisely to both technical and nontechnical stakeholders
Excels in collaboration, building trust and rapport with both technical and non-technical stakeholders.
Relentless in delivering high-quality products, even under pressure.
Understands that speed and agility are key competitive advantages and drives urgency and efficiency without compromising quality.

Nice to Haves

Experience finetuning largescale transformer models (BERT, GPT) and promptengineering for sentiment tasks
Background building activelearning and annotation pipelines to bootstrap training data
Familiarity with semantic search or vector databases (Elasticsearch, FAISS, Pinecone) for topic modeling and similarity queries
Familiarity with crypto markets, order books, and risk-management frameworks
Familiarity with anomalydetection methods for streaming text and timeseries data
Experience developing EVM smart contracts with Solidity and modern toolchains (Foundry or Hardhat).
Experience with realtime subscription frameworks (GraphQL subscriptions, WebSockets) or gRPC streaming for live data updates

Seniority level

Seniority level
Mid-Senior level

Employment type

Employment type
Full-time

Job function

Job function
Engineering and Information Technology
Industries
Entertainment Providers

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