Build streaming and batch pipelines on Kafka/Redpanda and a time-series store (ideally ClickHouse) with a lakehouse architecture, using Python, SQL, Docker, Terraform, and CI/CD. You will own data contracts, schema evolution, and governance for the Central Data Team building the Keyrock Data Platform that serves trading desks, wealth/asset management, risk, and compliance.

Build and validate simulation infrastructure for large-scale ML systems, modelling compute, memory, and communication behavior for training and inference. Work with Python, C++, or Rust; preferred tools include PyTorch, JAX, TensorFlow, NCCL, XLA, CUDA, and networking technologies like InfiniBand and NVLink. The role involves performance benchmarking, distributed execution across accelerators, and cross-functional partnership with hardware, software, networking, and ML teams.

Design and build fault-tolerant, high-performance systems for serving LLMs, working with Python, Go, Rust, or C++, Docker, Kubernetes, AWS, or GCP, and LLM serving frameworks.

You'll work with Python, Rust, ONNX, Chalk.ai, and GCP (Kubernetes, BigQuery, Agent Platform Endpoints). The team productionizes 10+ ML models for route assignment, delivery prediction, and

You will build and own infrastructure using Linux, AWS, Terraform, CI/CD, Python (Go, Rust, C++, Java beneficial), PostgreSQL, Redis, Kafka, and ClickHouse. Your team builds real-time intelligence systems for global financial markets, combining ML, HPC, and systematic trading. This is a founding role with direct impact on live trading infrastructure.

Fidelity Investments★4.0 (5.1k)· London

Use Rust, Python, SQL/Snowflake, and blockchain data tools (The Graph, Dune) to build research infrastructure and develop yield-generating DeFi strategies. You will extend backtesting systems and implement on-chain analytics for the Quantitative Strategies team within Fidelity Digital Assets™.

Genius Sports★4.4 (37)· London

You will use Python (or R, Matlab) and machine learning algorithms such as neural networks and Monte Carlo simulation, with MLOps via Gitlab, AWS, and Docker. Your multidisciplinary Agile team develops predictive models that calculate sports outcome probabilities for Genius Sports’ live data products.

Genius Sports★4.4 (37)· London

You will work with Python, R, Matlab, Gitlab, AWS, Docker, Rust, C++ and machine learning algorithms (Monte Carlo, neural networks, logistic regression, random forest). The team develops predictive models that calculate sports outcome probabilities, serving as the sporting brain behind Genius Sports’ customer-facing products.

Design and scale infrastructure for CoreWeave's GPU Intelligence Platform using Python, Go, Rust, or C++. Build data ingestion, feature pipelines, and deploy ML models on Kubernetes with streaming tools like Kafka. The team develops a reliability platform for fleet-level anomaly detection, prediction, and automated root cause analysis.

You will work with C++, Rust, Python, TypeScript, and protocols like FIX, WebSocket, and HTTP to build ultra-low-latency event-driven trading systems. The team develops Flow Traders' proprietary trading platform, covering venue connectivity, trading engines, and shared platforms for high-frequency trading across hundreds of global venues.

Nebius★0.0· London

You will design and operate large-scale indexing systems and data pipelines using Go (with C++/Rust as a plus) for an agent-native search platform that processes tens of gigabytes per second. The role focuses on offline and nearline data ingestion, processing, and storage optimization for AI agents retrieving real-world information at scale. You'll collaborate with runtime and ML teams to ensure indexing outputs meet retrieval and ranking requirements.

You'll lead Relation's Data Platform team, building scalable data infrastructure using Python or Rust, Nextflow, Seqera Platform, Dagster, and cloud systems (AWS/GCP/Azure) to integrate wet-lab instruments with computational pipelines. The role manages end-to-end data flow, enforces FAIR data governance standards, and oversees scientific compute infrastructure for a TechBio company discovering medicines through single-cell multi-omics and machine learning. You'll hire and develop a high-performing data engineering team while partnering across labs, engineering, data science, and ML functions.

You'll work with Python, PyTorch, TensorFlow, and JAX to build retrieval, ranking, and classification models for Perplexity's advanced search platform, including RAG pipelines and LLM-based evaluation tools. The internship (12–24 weeks, full-time, in-person in London) involves improving search quality through experiments, model training, deployment, and cross-team collaboration on production ML systems.

XTX Markets seeks a Machine Learning Performance Engineer to optimize training and inference platforms using C/C++/Rust, CUDA, and compiler development for GPUs and TPUs. The role involves building optimizing compilers for accelerated computing, working with JAX graphs, and potentially designing novel hardware architectures for an algorithmic trading firm that processes $250bn daily across 50,000+ financial instruments using deep learning models.

You'll build cloud-native, high-throughput distributed systems for real-time market data processing using technologies like Kubernetes, Kafka, and modern data tools (Polars, Arrow, Flight/gRPC), with optional C++/Rust expertise. The Tick Data Platform team, newly formed and small, creates globally distributed market data architecture supporting QRT's quantitative research and trading operations. You'll collaborate with researchers and data scientists to architect scalable platforms from the ground up.

You'll build and enhance front-office systems for QRT's systematic crypto trading desk using Python (C++ or Rust a plus), developing risk engines, productionizing models, and supporting 24/7 algorithmic trading strategies. The role involves designing clean architecture, collaborating with traders and quants, and experimenting with new technologies across a quantitative investment manager operating in all liquid asset classes globally.

You'll build front-office trading systems including portfolio management, risk engines, and reconciliation tools using Python with C++ or Rust, supporting QRT's systematic cryptocurrency trading strategies. The role involves designing scalable, real-time architectures and collaborating with traders and quants to automate 24/7 trading operations at a global quantitative investment firm.

You'll work with TypeScript, JavaScript, Node, NestJS, React, and Docker on web-based trading platform GUIs used globally by traders and IT staff. DRW is a diversified trading firm operating across Fixed Income, ETFs, Equities, FX, Commodities, Energy and alternative strategies like real estate and crypto. You'll join a small development team handling both front-end and back-end code with direct user feedback.

You'll build and maintain Python services that process and publish financial metrics for Sesame, Landytech's investment reporting platform used by asset managers and advisors across 15+ countries. The role involves developing APIs, designing data pipelines, and optimizing systems for financial data, with exposure to modern data processing libraries and fintech domain knowledge valued but not required.

You'll develop and maintain Python-based data pipelines and frameworks using SQL, Postgres, and data transfer protocols like HTTP APIs and AWS S3 for Squarepoint's Alpha Data team, which ingests and transforms vast quantities of data served globally to quant researchers and traders. The role involves designing resilient pipelines, collaborating with researchers, and contributing to frameworks and services that improve team efficiency and code quality.