Top Python Jobs in London

Lead the platform and AI product strategy for agentic commerce, integrations, retail media and product data. Define and deliver revenue-driven roadmaps, build APIs and commerce protocols for agent and partner access, ensure trust and safety, improve product data quality, automate product operations with AI, and coordinate engineering, design and commercial teams to hit growth targets.

Monitor security alerts from Defender, SIEM and other tools, perform initial triage and analysis, document and escalate incidents, maintain SOC tooling, follow SOPs, join shift rotations, and support SOC-as-a-Service growth while staying current on threats.

Pre-sales Solutions Architect bridging product and customers: design and present AI/quantum solutions, run demos, write proposals/RFP responses, architect cloud/on-prem/hybrid AI deployments, implement MLOps and containerized LLM inference stacks, size GPUs, benchmark LLM performance, and travel as needed for customer engagements.

Manage development, operation and validation of regulatory and economic capital models (SCR/EC/Standard Formula) for Lloyd's syndicates. Produce capital analyses, communicate results to senior stakeholders and regulators, support ORSA and portfolio optimisation, oversee controls and documentation, and lead and coach the capital modelling team.

Execute end-to-end software release validation and regression testing on physical humanoid robots and in simulation. Design and run policy evaluations, compare sim-to-real behavior, collect reliability and performance metrics, maintain CI/CD test suites and dashboards, and ensure test data quality to determine release readiness.

Develop and train language-vision conditioned manipulation policies with reinforcement learning in simulation and real robots. Build diverse simulated task suites, collect teleoperation trajectories for behavior cloning, establish real-world RL training pipelines, and research sim-to-real transfer methods.

Lead ownership of ML models and infrastructure lifecycle: develop, deploy, monitor, and improve ML systems. Build CI/CD for software and ML workflows, automate orchestration and environment management, ensure scalability, reliability, observability, and security, troubleshoot production issues, and document operational procedures while collaborating with engineers, data scientists, and product teams.

Lead development and integration of production-grade, deterministic control software for humanoid robots. Design real-time control architectures, implement high-performance control loops, industrialize algorithms, ensure safety and reliability, run automated testing (simulation and hardware-in-the-loop), manage releases, profile and optimize performance, and mentor engineers.

Design, simulate, and validate bipedal and wheeled robot platforms using physics simulation and reinforcement learning. Own URDF models and the CAD->URDF->simulation pipeline, develop benchmarks for locomotion/manipulation, analyze dynamics and actuator sizing, and provide quantitative feedback to hardware, control, and perception teams.

Build and maintain device software and data pipelines for handheld gripper data collection devices. Implement sensor integration and temporal synchronization, recording/packaging episodes, validation tooling to detect data faults, operator-facing controls, and on-site support. Debug hardware integration issues and collaborate with hardware, AI, and data teams to ensure high-quality training datasets.

Own quality for the humanoid software platform: test the operator console, inference infrastructure, and backend services. Design and implement automated CI/CD test suites, run contract/integration/performance validations, define quality metrics and strategy, and document tests and processes.

Provide remote diagnostics and resolution for humanoid robots using telemetry and digital diagnostics. Produce precise physical-intervention plans for field or repair teams, escalate to engineering when needed, prioritize SLAs, document cases, and occasionally travel onsite as a "flying doctor."

Develop and iterate on pre-training and fine-tuning pipelines for VLA policies, own data-to-deployment loops, partner on data collection, scale distributed training, curate datasets and synthetic data, and optimize models for real-time edge inference.

Build backend and on-robot services to author, deploy, and manage fleet workflows. Implement workflow state management, event logging, APIs, data infrastructure, cloud deployment, observability, testing, and simulation-based validation. Collaborate with robotics, AI, and product teams to create operator-facing tooling and reliable production systems.

Optimize and deploy neural-network control policies for humanoid robots: profile and remove inference bottlenecks, port models to new hardware, implement custom kernels, quantize models with minimal quality loss, and propose architecture changes to improve latency and memory usage.

Design and implement a cloud platform to manage a fleet of humanoid robots, build backend APIs and robot-side services, deploy secure infrastructure (CI/CD, containers, IaC), implement observability and testing pipelines, automate workflows, and maintain documentation.

Own software serviceability for humanoid robots: design and validate diagnostics, firmware/bootloader recovery, remote-diagnostics content, tooling for fast fault localization, and act as Services interface to R&D. Validate OTA, calibration, and repair procedures and support new service introductions.

Design, train, and validate multimodal, action-conditioned world models (video, state, action, language) for humanoid robots. Develop learned-simulator evaluation, generate synthetic rollouts for sim-to-real and policy training, build data pipelines from fleet telemetry, run large distributed training and scaling studies, and collaborate with pretraining, RL, and manipulation teams to integrate models into policy and safety workflows.

Build teleoperation systems enabling human operators to control, recover, teach, and improve humanoid robots in real time. Design operator UX, integrate input devices, translate human input to safe robot commands, implement shared-autonomy, logging, replay, and safety safeguards, and test systems on hardware and in simulation to drive reliability and quality metrics.

Lead end-to-end pretraining of vision-language-action foundation models: define architecture and data mixtures, run large-scale distributed training, build evaluation and continuous pretraining pipelines, secure diverse multi-embodiment data, and mentor a team to deliver base models that enable downstream robot capabilities.

Lead design and implementation of SLAM, state estimation, long-term mapping, and localization systems for humanoid robots. Integrate modern ML and foundation geometry models into navigation pipelines, build auto-labeling data workflows, deploy and benchmark spatial AI on edge hardware, and bridge research to production for robust, low-latency robot navigation in dynamic real-world environments.

Develop and maintain embedded Linux platform software and core subsystems for humanoid robots, integrating sensors and safety systems, building kernel/device drivers, ensuring performance and reliability, leading cross-functional projects, and contributing infrastructure and safety practices for real-world deployments.

Build and maintain physics-based simulation environments and RL infrastructure for dexterous manipulation across industrial, service, and home domains. Design Gym-style observation/action/reward spaces, mitigate reward hacking, reduce sim-to-real gap, validate physics against real data, integrate high-fidelity 3D assets, optimize simulation throughput, and support large-scale GPU-accelerated training.

The Senior Site Reliability Engineering Manager oversees SRE teams across regions, ensuring high availability of trading platforms, leading projects, and managing incident responses.

Customer-facing senior engineer who architects, builds, and productionizes end-to-end data and AI solutions on the Databricks platform. Leads design decisions, implements data pipelines, integrates ML/AI models, collaborates cross-functionally, delivers technical projects, embeds with customer teams, and contributes reusable assets to scale impact.