Top Python Jobs in London

Administer and maintain endpoint security platforms (Tanium & SentinelOne), deploy and manage Tanium modules, monitor performance, build dashboards and reports, perform audits and compliance checks, troubleshoot endpoint visibility/patching/deployments, integrate endpoint tools with IT/security systems, and provide training and support.

Lead and execute multi-vector red team campaigns including adversarial emulation, post-exploitation, social engineering, and physical testing. Develop tools, exploit vulnerabilities, produce comprehensive technical reports with remediation guidance, present findings to technical and non-technical stakeholders, mentor team members, support business development, and contribute research to the security community. Requires significant travel.

Hands-on Tech Lead responsible for end-to-end ownership of AWS-native services, driving architecture, code quality, CI/CD, IaC (Terraform), observability, and reliability. Mentor engineers, reduce technical debt, embed security, and collaborate with Product and Architecture to deliver scalable, cost-efficient solutions.

An entry-path Master Class to pivot into life sciences consulting. Deliver customer engagements, run workshops and stakeholder interviews, analyze data using Veeva's Data Cloud, create operational and analytics-driven solutions, collaborate with consultants, and travel up to 20% annually. Anticipated start April 2027.

Build and maintain low-latency, high-throughput backend services and data pipelines; own service reliability, observability, incident response, and automation; operate streaming and data infrastructure; extend IaC and CI/CD; reduce toil and improve production resilience.

Design, build and optimise ETL/ELT pipelines and data foundations across enterprise systems. Profile, cleanse, map and reconcile data, implement quality frameworks, develop scalable data models on cloud platforms, and work with stakeholders to establish repeatable data architecture, governance and reporting for transformation programmes.

Build end-to-end full‑stack products: design and ship frontend, backend, and infrastructure; integrate AI/ML and LLMs; own production systems for performance, reliability, and iteration; prototype, run experiments, and lead technical decisions alongside engineering leadership.

Senior individual-contributor role owning architecture for the companys most complex systems. Lead design and production of full-stack, AI-native platforms (LLM workflows, intelligent pipelines), set engineering standards, mentor engineers, and translate ambiguous product bets into concrete technical plans while preventing systemic technical risk.

Design and build internal systems and tools across the firm, including infrastructure, monitoring, and tooling used by trading, research, and support teams. Collaborate with engineers, contribute to open source projects, and solve practical problems using strong programming and problem-solving skills.

Manage the firms equity and futures liquidity programme: oversee liquidity intelligence, analyse execution and market structure, run expansion roadmap, coordinate stakeholders and external partners, prepare governance reporting, and drive low-latency and venue/product initiatives to inform liquidity strategy.

Design, develop, and deploy scalable ML and time-series forecasting engines integrated into Anaplan applications. Lead customer onboarding and customize models to client data, build feature pipelines (SQL/Python/dbt), and operationalize models via MLOps tools (MLflow, Optuna). Collaborate with Product, Engineering, and Customer Success to deliver production-grade, monitored solutions.

Build and maintain high-fidelity simulation environments (6-DOF, aerodynamics, structures, engagement-level), run Monte Carlo and sensitivity analyses, validate models against flight/test data, support design trade studies and flight/range testing, and build tooling/infrastructure to make simulation accessible across engineering teams.

Act as the technical bridge between Sales, Customers, and Engineering to support pre-sales consultation and post-sales integrations. Guide DSP integrations, troubleshoot campaign and data issues, manage campaign feature deployments, monitor performance, document issues, and enable customers with technical best practices to ensure successful product adoption.

Lead causal inference and econometric analysis to measure impacts of pricing, products, and policy. Define experimental and quasi-experimental standards, design structural models, and build scalable metric trees using optimized SQL and Python. Partner with business leaders, deliver executive-grade narratives about statistical uncertainty, and establish verification frameworks for safe AI-assisted analysis.

Design, build, and maintain scalable ML models and optimization systems for production. Own end-to-end projects, implement numerical optimizers (LP/MIP), write production-grade Python and SQL on Databricks, monitor drift, and mentor peers to ensure robust, interpretable, and maintainable ML services.

Design, develop, and support scalable software solutions using TypeScript, JavaScript and Angular. Work independently in an Agile team, write clean code and unit tests, troubleshoot performance and integration issues, collaborate with product and stakeholders, and contribute to continuous improvement and AI-enabled initiatives.

Develop and optimize a learning-based robotic manipulation control stack; design and maintain low-latency teleoperation systems; train and deploy policies using reinforcement and imitation learning; diagnose control-pipeline latency and sim-to-real gaps; co-optimize simulation and real-world robot behavior; collaborate on building general-purpose Physical AI.

Design, build, and optimize distributed PyTorch training systems for multi-node GPU clusters. Profile and eliminate performance bottlenecks across data pipelines, kernels, and networking. Implement low-level CUDA/cuDNN/Triton kernels, tune hardware-software interactions, and develop monitoring and debugging tools for large-scale training runs to maximize utilization and reduce time-to-convergence.

Design, build, and operate large-scale distributed systems to improve reliability, scalability, and efficiency. Automate toil, perform capacity planning and performance analysis, prevent incidents, and provide cross-team technical leadership to balance system health with product velocity.

Design and implement low-latency, high-throughput inference pipelines for on-device and GPU-cluster deployment. Optimize kernels, batching, quantization, memory and scheduling. Build distributed serving systems, integrate low-level CUDA/Triton code with high-level frameworks, and develop monitoring/debugging tools to ensure reliability and determinism.

Design, build, and maintain petabyte-scale batch and streaming data pipelines for robotics foundation model training and evaluation. Own data models, storage, ingestion, transformation, and orchestration. Standardize data models and unify processing across teleoperation and simulation datasets. Collaborate with a team building general-purpose Physical AI.

Design, build, and maintain foundational frameworks and internal tooling to accelerate AI research workflows. Collaborate with teams to create modular, reusable production-ready systems, remove cross-team friction, improve development workflows, and promote high code quality through reviews, documentation, and training.

Manage curriculum development for data science and engineering, collaborate with instructors, create content, and drive improvements based on learner feedback.

As a Curriculum Manager, you will manage content development for the data science and engineering curriculum, collaborating with instructors and teams to enhance learning experiences.

Build and productionize ML systems for personalization, search, and recommendations. Apply statistical and ML models using Python and SQL, collaborate with stakeholders, and measure business impact across cloud environments.