Senior Software Engineer About the Company We're partnered with a stealth-stage robotics and embodied AI company building toward production-grade physical systems. They sit at the intersection of two demanding worlds, high-performance distributed computing for AI research and real-time execution on resource-constrained robot hardware, and are now hiring the founding members of their Platform Team. About the Role As a founding member of the Platform Team, you'll architect and build the software backbone of the company. The software needs to span two very different worlds, high-performance distributed computing for AI research (training, massive data ingestion, RL simulation) and resource-constrained, real-time execution on physical robot hardware. This is a high-impact, greenfield opportunity. You won't be maintaining legacy code, you'll be making critical architectural decisions that define how the company scales from prototype to production. You'll act as the bridge between research scientists and hardware, ensuring that state-of-the-art models can be trained efficiently and deployed reliably to the real world. Your ResponsibilitiesArchitect and build. Design and implement a scalable software platform that unifies research workflows (training, simulation) with production realities (real-time inference, data collection).Bridge the gap. Develop seamless tooling that facilitates the transition of models from Python-heavy research environments to performant C /Rust runtimes on hardware.Performance optimization. Optimize the stack's critical path, focusing on inference latency, distributed training throughput, and system resource management.Infrastructure and tooling. Establish engineering excellence by setting up robust CI/CD pipelines, build systems (Bazel), and containerization strategies (Docker).Reliability. Engineer fault-tolerant systems capable of handling long-running experiments and safety-critical operations on physical robots.Essential SkillsEducation. MS in Computer Science or a comparable technical field.Software engineering. 5 years shipping high-quality software, with a track record of owning large features from design through deployment.Language proficiency. Expert-level fluency in Python (for tooling and ML infrastructure), plus strong proficiency in either modern C or Rust.System architecture. Demonstrated experience designing scalable software architectures, including microservices, API design (gRPC/REST), and distributed systems.Engineering rigor. A commitment to automated testing, code reviews, and writing maintainable, modular code.Beneficial SkillsMachine learning systems. Experience building ML frameworks (PyTorch), MLOps infrastructure, data pipelines, or deploying models.Build and deploy. Hands-on experience with Docker and Bazel. Experience with orchestration (Kubernetes) or job schedulers (SLURM) is a plus.Robotics middleware. Familiarity with ROS2, DDS, or similar message-passing frameworks.Cloud infrastructure. Experience managing compute resources on AWS, GCP, or Azure using Infrastructure-as-Code (Terraform, Ansible).Simulation. Experience integrating with simulation environments (Isaac Sim, MuJoCo) for Reinforcement Learning.

Senior IT Infrastructure Engineer About the Company We're partnered with an AI and robotics company building infrastructure from the ground up to support advanced development workflows in machine learning and embodied AI. They're at a stage where the IT foundation is being established and shaped, giving this role unusual scope and ownership over the long-term direction of the environment. About the Role As an IT Infrastructure Engineer, you'll be responsible for establishing infrastructure from the ground up, including capacity planning, disaster recovery, and day-to-day operations. You'll manage, configure, and monitor the company's IT infrastructure, including automated backups, ensure the security and availability of resources, and work closely with engineering and operations teams to provide a robust, scalable IT environment that supports AI and robotics development workflows. Your ResponsibilitiesInfrastructure architecture and operations. Design, implement, and maintain on-premise IT infrastructure across compute, storage, and networking. Perform capacity planning, develop and execute backup and disaster recovery strategies, and maintain comprehensive infrastructure documentation.Physical data center and cloud infrastructure. Manage and monitor on-premise IT facilities (servers, cooling, power) and hardware. Design and provision storage and compute/GPU infrastructure for high-performance ML and AI workloads.Enterprise networking. Design and implement WAN/LAN/WiFi network topology with proper segmentation and security controls (firewalls, IDS/IPS). Configure and manage enterprise networking equipment including switches, routers, and load balancers.System administration and support. Deploy and manage Linux server infrastructure. Configure and deploy employee workstations across Linux, macOS, and Windows, and manage IT equipment procurement. Provide technical troubleshooting and support, and manage user accounts with SSO.Vendor management. Establish and manage relationships with technology vendors, negotiate contracts, and coordinate with service providers including ISPs and colocation partners.RequirementsProven track record in building or transforming infrastructureDeep expertise in enterprise networking (WAN/LAN, VLANs, routing, switching, firewalls, VPNs)Strong hands-on experience with server hardware assembly, configuration, and maintenanceExpert knowledge of storage (RAID, SAN/NAS) and backup and recovery solutionsExperience with Linux server administration and troubleshootingSolid understanding of data center operations (power, cooling, security)Hands-on experience provisioning and managing GPU infrastructureScripting skills in Python and Bash for automationExperience with Infrastructure-as-Code tools such as Terraform and AnsibleStrong problem-solving and troubleshooting skills for complex hardware and network issuesExcellent documentation and communication skillsSelf-motivated and able to work independently in a fast-paced environment

Training Infrastructure Engineer About the Company We're partnered with a generative AI lab building the next generation of creative tools by producing realistic sound, speech, and music from video. They're developing cutting-edge foundational generative models that "unmute" silent video content and create custom, hyper-realistic audio for gaming, video platforms, and creators, empowering global storytellers to transform their content. They recently closed a $41 million Seed round co-led by two top-tier US venture firms, with participation from a leading global investor, and are rapidly expanding across Product, Engineering, Go-to-Market, and Growth. About the Role You'll focus on the full training stack, profiling GPU behavior, debugging training pipelines, improving throughput, choosing the right parallelism strategies, and designing the infrastructure that lets the team train models efficiently at scale. The work spans cluster management, model training, efficient data pipelines for video and audio, inference, and optimizing PyTorch code. Your contribution will shape the foundation on which all of their generative models are built and iterated. Key ResponsibilitiesIdentify ideal training strategies (parallelism approaches, precision trade-offs) for a variety of model sizes and compute loadsProfile, debug, and optimize single and multi-GPU operations using tools like Nsight and stack trace viewers to understand what's actually happening at the hardware levelAnalyze and improve the entire training pipeline end to end, including efficient data storage, data loading, distributed training, checkpoint and artifact saving, and loggingSet up scalable systems for experiment tracking, data and model versioning, and experiment insightsDesign, deploy, and maintain large-scale ML training clusters running SLURM for distributed workload orchestrationIdeal Candidate ProfileFamiliarity with the latest and most effective techniques for optimizing training and inference workloads, not from reading papers but from implementing themDeep understanding of GPU memory hierarchy and computation capabilities, knowing what the hardware can do in theory and what prevents you from achieving it in practiceExperience optimizing for both memory-bound and compute-bound operations, with a clear sense of when each constraint mattersExpertise with efficient attention algorithms and their performance characteristics at different scalesNice to HaveExperience implementing custom GPU kernels and integrating them into PyTorchExperience with diffusion and autoregressive models and an understanding of their specific optimization challengesFamiliarity with high-performance storage solutions (VAST, blob storage) and their performance characteristics for ML workloadsExperience managing SLURM clusters at scaleWhy Join?Pivotal moment. Fresh funding is secured and traction is building, this is the point where your contributions can make a real difference to the company's trajectory.True ownership from day one. Genuine autonomy and responsibility, with ideas and work that directly shape both product and company direction.Competitive compensation and equity. Strong packages that ensure you share in the success you help create.Build for the next generation of creators. Be part of the innovation that will transform how creators work and thrive.

Founding Member of Technical Staff (Platform Engineering) I’m currently partnered with a well-funded, early-stage applied AI company building at the frontier of reinforcement learning and agentic systems for financial services. They’re working closely with leading AI labs to train models capable of performing complex, real-world tasks (think financial modelling, investment workflows, and other high-value knowledge work). The business has recently come out of a top-tier accelerator and raised seed funding, and is now scaling its founding engineering team. The Opportunity They’re looking to bring in Founding Members of Technical Staff to take ownership of platform engineering and play a key role in shaping both the product and engineering culture from day one. What You’ll Be DoingBuilding and scaling RL training inference infrastructure for real-world usageDesigning long-horizon, high-fidelity RL environments to push frontier modelsImproving the speed and quality of environment creation (10–100x focus)Developing synthetic data pipelines to simulate complex financial scenariosCreating analytics tooling across performance, cost, and operational workflowsBuilding domain-specific evaluation systems (e.g. for modelling, presentations, trading)Helping define engineering standards, culture, and ways of workingWhat They’re Looking ForExperience with RL, evaluations, or benchmarking for AI systemsStrong startup mentality — high ownership, fast iteration, low egoProduct-oriented engineers who can prioritise and think commerciallyComfortable engaging with users, customers, and domain expertsNice to HaveResearch background (e.g. training models, published work)Previous startup or founding experiencePackage$150k–$250k base meaningful equity (0.25–0.50%)Strong benefits package (healthcare, meals, gym, transport, etc.)Relocation support visa sponsorship availableLocation San Francisco (5 days onsite)

We are supporting a global engineering and technology consultancy on a senior AI hire in London. Although the title is Principal AI Engineer, the role is probably better described as a Founding AI Solutions Engineer or Forward Deployed AI Engineer style position. They are looking for someone who can sit between hands on AI engineering, solution architecture, and client facing consulting. The context is interesting. The business is seeing strong demand from enterprise clients around AI engineering, GenAI, automation, and production AI use cases. They now need a senior AI hire in the UK who can help bring this capability together. The role would involve building AI solution accelerators, shaping client demos, running technical workshops, and delivering production grade AI systems across areas like LLMs, RAG, vector databases, classical ML, MLOps, cloud deployment, and data pipelines. It is not a pure pre sales role, and it is not a strategy only consulting role. They need someone who can still build, but also explain technical choices clearly to customers and senior stakeholders. The split is roughly 60 percent hands on engineering and 40 percent client facing solutioning. They are looking for someone with: • Strong AI, ML, or GenAI engineering experience • Python and production engineering depth • Experience taking AI systems into live environments • Exposure to LLMs, RAG, vector databases, MLOps, or cloud AI delivery • Confidence running demos, workshops, or customer facing technical conversations • Consulting, FDE, solutions engineering, or enterprise delivery experience

I wanted to reach out as I’m supporting a global engineering and technology consultancy on a senior AI hire in London. Although the title is Principal AI Engineer, the role is probably better described as a Founding AI Solutions Engineer or Forward Deployed AI Engineer style position. They are looking for someone who can sit between hands on AI engineering, solution architecture, and client facing consulting. The context is interesting. The business is seeing strong demand from enterprise clients around AI engineering, GenAI, automation, and production AI use cases. They now need a senior AI hire in the UK who can help bring this capability together. The role would involve building AI solution accelerators, shaping client demos, running technical workshops, and delivering production grade AI systems across areas like LLMs, RAG, vector databases, classical ML, MLOps, cloud deployment, and data pipelines. It is not a pure pre sales role, and it is not a strategy only consulting role. They need someone who can still build, but also explain technical choices clearly to customers and senior stakeholders. The split is roughly 60 percent hands on engineering and 40 percent client facing solutioning. They are looking for someone with: • Strong AI, ML, or GenAI engineering experience • Python and production engineering depth • Experience taking AI systems into live environments • Exposure to LLMs, RAG, vector databases, MLOps, or cloud AI delivery • Confidence running demos, workshops, or customer facing technical conversations • Consulting, FDE, solutions engineering, or enterprise delivery experience

Lead Python EngineerWe’re working with an early-stage AI product company in London building software that automates real-world operational workflows using Python and applied AI.This is a hands-on engineering role focused on designing and building backend systems, APIs, and data-driven workflows that power AI features used in production. You’ll work closely with product and design to take ideas from concept to live features, integrating LLM capabilities into practical use cases.The role is around 80 percent coding and 20 percent leadership. Alongside building systems, you’ll mentor engineers, contribute to code reviews, and help shape technical decisions as the team grows.You’ll be responsible for:Designing and building scalable backend services using PythonDeveloping APIs that support core product functionalityIntegrating LLM and AI-driven features into workflowsImproving system performance, reliability, and maintainabilityCollaborating cross-functionally to deliver features end to endKey requirements:4 to 8 years software engineering experienceStrong Python backend skills, APIs, and frameworks such as FastAPI, Flask, or DjangoExperience building production systems in a startup or fast-paced environmentFamiliarity with cloud platforms such as AWS, GCP, or AzureExposure to AI, LLMs, or RAG is beneficialYou’ll join a small, product-focused team with high ownership and the opportunity to influence both engineering direction and product development.

AI Data Engineer – Python, Snowflake & Machine Learning (Contract, Remote) We’re looking for an experienced AI Data Engineer to build scalable data pipelines and deploy production-ready machine learning solutions within a modern cloud data platform. The role: Own end-to-end data and ML workflows, from ingestion and transformation through to model deployment and monitoring, with a focus on reliability and scalability in Snowflake and Python. What you’ll be doingBuild and maintain data ingestion pipelines into Snowflake (structured and time-series data)Prepare ML-ready datasets (feature engineering, aggregations, train/test splits)Develop, train, and deploy ML models using Python (scikit-learn, XGBoost, LightGBM)Operationalise ML workflows in Snowflake using SnowparkWrite model outputs back to Snowflake for downstream useMonitor pipelines and models, including data quality checks and retraining triggersTechnical environmentSnowflake (SQL, Streams, Tasks, Snowpipe)Python for data engineering and ML (including Snowpark)ML frameworks: scikit-learn, XGBoost, LightGBMTime-series data processing (desirable)Azure Data Lake / Microsoft Fabric (nice to have)Contract detailsInitial contract with strong extension potentialFully remote (Has to be European based) €300 - €325 Per Day