Junior Remote Remote Remote Remote Remote Software Engineer 2c Devops Specialist in Amsterdam

About BlockTech BlockTech is a fast-paced algorithmic trading firm at the frontier of global cryptocurrency derivatives and spot markets. We trade 24/7 across some of the most data-rich, fast-moving venues in finance, and we use that data to build smarter models, sharper signals, and more adaptive systems. Crypto is one of the few markets where a researcher can still meaningfully move the edge. The data is abundant, the microstructure is novel, and the feedback loop between a research idea and live PnL couldn’t be shorter. We’re growing fast, and we’re looking for a Quantitative Researcher with a strong machine learning toolkit to help us push that edge further. The role As a Quantitative Researcher on our trading floor, you’ll own ideas end-to-end from hypothesis and dataset construction through feature engineering, model training and backtesting, all the way to live deployment, monitoring, and iterative improvement. You’ll sit shoulder-to-shoulder with Quantitative Traders and Quantitative Analysts, and your work will directly drive how we price and trade. You will: Collaborate closely with traders to translate research insights into systematic trading strategies Design, develop and deploy models for price prediction, signal generation, execution, and anomaly detection across crypto derivatives and spot markets using state-of-the-art AI and ML techniques. Build robust trading, backtesting and research infrastructure alongside our engineers, so promising ideas can move into production quickly and safely Own models in production: monitoring live performance, diagnosing decay, and iterating on what you ship What we're looking for A strong academic background in a quantitative discipline (Mathematics, Physics, Statistics, Computer Science, Econometrics, ML/AI, or similar), typically a PhD or an MSc with strong research experience Fluency in Python and the modern ML stack (PyTorch and/or TensorFlow, scikit-learn, NumPy, pandas) A deep, intuitive grasp of overfitting, g

About BlockTech BlockTech is a fast-paced algorithmic trading firm facilitating global cryptocurrency derivatives and spot trading while expanding into new markets. As we continue to grow rapidly, we are looking for a Software Engineer to join our Core team amid our exciting scale-up phase! The role In this role, you will work on the systems that keep our trading operation accurate at every level, positions, PnL, instruments, hedging, and the data flowing between them. Across exchanges and asset classes, your work determines whether the numbers our traders and risk team rely on are correct. Own the books. Maintain and evolve our position, trade, and PnL systems so they remain accurate across every venue we operate on. Build the platform that supports trading. Design services that handle real-time exchange flow with the throughput and reliability on which our quoting and hedging depend. Own the data flows. Operate the public and private data pipelines that bring market prices, account state, and trade events into our systems in real time. Integrate new venues. Deliver exchange and prime-broker integrations end-to-end, protocol, account model, settlement, reconciliation, and surveillance. Model the instrument universe. Own the systems that describe every tradable instrument we trade, lifecycle, expiry, margin, RFQs, fast-path quoting, where both correctness and latency matter. Develop self-healing systems. Build the detection and repair flows that identify position discrepancies and correct them automatically. Build hedging infrastructure. Design and refine the gamma and delta hedging systems that our trading strategies depend on. Deliver reporting. Provide the data that internal stakeholders and regulators require, PnL breakdowns, trade surveillance, treasury accounting, and compliance. What we're looking for 1+ years of commercial working experience as a Software Engineer. We are looking for an engineer with breadth: someone comfortable moving between a Rust service