London | Remote You’ll take ownership of the product that helps businesses build, train and deploy machine learning models at scale. This is an opportunity to lead product direction within a growing AI infrastructure business. You’ll define the longer-term vision, decide what gets built and work closely with engineers, ML specialists and customers to improve how AI models are developed and operated. The product covers some of the biggest challenges in machine learning infrastructure: how models are trained and fine-tuned, how GPU resources are used, and how models are deployed and served efficiently in production. You’ll have genuine product ownership. Rather than simply managing a delivery backlog, you’ll shape the roadmap, identify opportunities to improve the platform and turn complex technical and customer requirements into clear product decisions. You’ll also work closely with engineering and customers, gaining direct insight into how organisations are building and deploying AI and using that feedback to influence the future direction of the platform. This could suit you if you’re already a Technical Product Manager working on ML platforms, cloud infrastructure, MLOps or developer tooling and want to take ownership of a product in a fast-moving AI market. You’ll need: 5+ years of Product Management experience, ideally owning technical platform or infrastructure products Experience with ML platforms, MLOps, AI infrastructure or cloud-based developer products A good understanding of machine learning workflows, including model training, deployment and inference Knowledge of technologies such as Kubernetes, PyTorch, Ray, Slurm or similar Experience defining product strategy, building longer-term roadmaps and working closely with engineering teams and technical customers A strong technical understanding through education or hands-on experience Experience with GPU infrastructure, distributed computing or model serving would be particularly relevant. London | Remote If you’re interested in owning a product at the intersection of AI, machine learning and infrastructure, I’d be happy to tell you more.

Research Scientist / Research Engineer – LLM Post-Training & Reinforcement Learning Location: San Francisco Bay Area Industry: Frontier AI / Foundation Models Compensation: Highly competitive Seniority: Multiple levels The Opportunity We're working with an ambitious frontier AI research company in the Bay Area, building next-generation foundation models and exploring new approaches to advancing model intelligence, reasoning and computational efficiency. As part of their continued growth, they're looking to hire talented Research Scientists and Research Engineers specialising in LLM Post-Training and Reinforcement Learning. You'll have the opportunity to tackle complex, open-ended research challenges, contribute to the development of novel training methodologies and play a key role in shaping the capabilities of future foundation models. What You'll Be Working On The research spans several areas of modern foundation model development, including: Post-Training & Reinforcement Learning: Developing and improving RL-based training approaches, including PPO, GRPO, RLHF, preference optimisation and reward modelling. Large-Scale RL Systems: Building and optimising distributed training infrastructure, asynchronous training pipelines and high-throughput inference and rollout systems. Reasoning & Agentic Capabilities: Exploring methods to improve complex reasoning, multi-step decision-making, tool use and autonomous agent behaviour. Evaluations & Reward Design: Creating robust evaluation methodologies, synthetic environments and verifiable reward mechanisms to drive measurable improvements in model performance. Research & Experimentation: Taking ownership of research initiatives, from identifying promising ideas and designing experiments through to training, analysing results and implementing successful approaches. What We're Looking For We're interested in researchers and engineers who combine strong technical foundations with the ability to independently investigate and solve challenging research problems. Ideally, you'll bring: Practical experience training, fine-tuning or post-training large language models. Strong knowledge of reinforcement learning algorithms, particularly PPO, GRPO, RLHF and reward modelling. Experience with distributed training, large-scale GPU infrastructure or optimising RL training and inference workloads. Proficiency in Python, PyTorch and modern deep learning frameworks. Experience developing evaluations, designing experiments and interpreting model behaviour. A track record of meaningful contributions to AI research, whether through publications, open-source projects or production-scale model development. Experience with frameworks such as verl, OpenRLHF, TRL, NeMo RL, DeepSpeed or similar distributed training technologies would be particularly relevant. Why This Opportunity? This is a chance to join a research-driven organisation working at the forefront of foundation model development, where new ideas can directly influence the models being built. You'll work alongside highly capable researchers, have significant ownership over your research direction and contribute to solving fundamental challenges in AI. The team is expanding across multiple levels and welcomes both research-focused scientists and hands-on research engineers with experience developing and scaling advanced AI systems. Interested in pushing the boundaries of LLM post-training and reinforcement learning? Apply directly to us with your resume or get in touch for a confidential conversation to learn more about the company, research and opportunities available.

Summary An innovative quantum technology company is looking for a Quantum Success Engineer to support customers using advanced quantum control hardware and software. This role combines technical troubleshooting, customer onboarding and training, and relationship management. You will help researchers integrate quantum control systems into their experiments, resolve technical challenges and ensure they get the most from the technology, while sharing customer feedback with internal engineering and product teams. Responsibilities Customer onboarding and training: Support hardware installation, setup and integration, delivering technical training both onsite and remotely. Technical support: Troubleshoot customer issues involving quantum control systems, helping experimental research teams overcome technical challenges. Customer relationships: Build strong relationships with customers, respond to concerns and support customer satisfaction and long-term retention. Customer advocacy: Gather customer feedback and communicate insights to product and engineering teams to inform improvements and future development. Cross-functional collaboration: Work with internal technical and commercial teams to resolve issues and improve the overall customer experience. Requirements A PhD in experimental physics involving microwave or radio-frequency experiments, or a Master's degree in experimental physics with 1–2 years of relevant industry experience. Familiarity with Python programming. An understanding of electronics and experimental physics; experience with quantum computing is highly advantageous. Experience with embedded systems through academic projects or professional work is desirable. Strong problem-solving skills and a proactive, customer-focused approach. Excellent communication skills, with the ability to explain technical concepts clearly and professionally. Previous industry experience and/or experience in a customer-facing technical role is advantageous. Professional fluency in English. Benefits and opportunity Hybrid working: Three days in the office and two days working remotely. Generous annual leave: 30 days of paid holiday, in addition to national holidays. Competitive compensation: Attractive base salary and a 14% employer pension contribution. Company equity: Opportunity to participate in the company's long-term success through Stock Appreciation Rights. Relocation support: Relocation package and visa sponsorship for international hires and their families. Cutting-edge technology: Work at the forefront of quantum computing, supporting researchers and experimental teams worldwide. Career development: Collaborate with physicists, engineers and developers in a growing quantum technology business.

Summary An innovative deep-tech company developing next-generation quantum computing systems is seeking a Software Manager / Senior Software Manager to lead the team responsible for the software that controls and operates its hardware. With systems already deployed with customers, the company is focused on building a reliable, production-grade software platform that integrates quantum technology with existing HPC, GPU and data-centre infrastructure. This role combines software architecture, technical leadership and people management, with ownership of the platform, roadmap and customer-facing reliability. Responsibilities Technical leadership: Own the architecture and evolution of the low-level software stack, ensuring reliable integration with compute, networking, security and data-centre infrastructure. SDK and APIs: Lead the development of developer tools and APIs for hybrid quantum-classical applications. Team management: Lead, mentor and develop software engineers, setting priorities, standards and resource plans. Roadmap and delivery: Prioritise features and upgrades in collaboration with commercial, customer-facing and engineering teams. Performance and reliability: Improve latency, throughput, uptime and recovery in collaboration with hardware and optical engineering teams. Engineering quality: Establish best practices across testing, CI/CD, code reviews, release management, observability and incident response. Customer support: Lead technical escalations and root-cause analysis, ensuring issues are resolved effectively. Cross-functional collaboration: Promote documentation and shared ownership, and communicate technical progress to senior leadership. Requirements 10+ years of experience building and shipping production software, including leading engineering teams. Experience with HPC systems and/or large-scale data-centre infrastructure. Strong Python and C++ and/or Rust skills, with solid Linux systems knowledge. Experience with hardware-integrated software, such as real-time systems, instrument control, embedded software or FPGA integration. Strong engineering practices across testing, CI/CD, releases, monitoring and root-cause analysis. Experience supporting deployed customer systems and communicating calmly under pressure. Strong problem-solving, decision-making and prioritisation skills. A degree in Computer Science, Electrical Engineering, Physics or a related quantitative discipline, or equivalent experience. Desirable: Quantum computing, quantum control systems or scientific instrumentation. Digital signal processing, data acquisition, timing or synchronisation. Experience in high-reliability sectors such as semiconductors, telecommunications, medical devices, defence or aerospace. Experience scaling deep-tech products from R&D into commercial deployment. Benefits and opportunity Work on advanced quantum computing technology with real-world customer deployments. Shape the architecture, roadmap and reliability of a growing software platform. Combine technical decision-making with team leadership and development. Collaborate across software, hardware and optical engineering. Help bring next-generation computing technology into modern AI and HPC environments. Competitive salary and the opportunity to make a meaningful impact in an ambitious deep-tech business.

Research Scientist / Engineer, Inference San Francisco | Permanent DeepRec has partnered with a frontier AI research lab that designs its own model architectures from the ground up. Serving cost is treated as a research question here, so inference sits at the centre of the work rather than the end of it. The Opportunity You'll own latency and throughput across two serving stacks. One handles production traffic for customers. The other powers test-time compute and RL rollouts for the research team, so every gain you make speeds up the research itself.   What You'll Do Cut latency and raise throughput for model serving at very high concurrency Speed up RL rollout generation and scale test-time compute for research Tune batching, caching, load balancing and parallelism across the serving stack Write and profile GPU kernels in CUDA and Triton Apply quantization, distillation, speculative decoding and low-precision numerics Shape model and hardware co-design with serving cost in view from day one Own benchmarking, testing and reliability for every inference service What You'll Bring Essential Production experience with vLLM, TensorRT-LLM or similar at high concurrency Hands-on continuous batching and KV cache management Quantization for inference in production Profiling and performance analysis with CUDA and Triton Desirable Speculative decoding or multi-node serving Performance tuning on H100 or B200 hardware Kubernetes and production infrastructure We encourage you to apply even if you don't meet every requirement. The right mindset matters as much as the right CV.     Apply Now If you've spent your career making models fast, this is a place where that work shapes the research agenda. Send your CV and we'll share the full brief in a confidential conversation

Research Scientist / Engineer, Pre-training San Francisco | Permanent DeepRec has partnered with a frontier AI research lab building new model architectures, not fine-tuning someone else's. Pre-training is the foundation every product and post-training effort builds on, and the team concentrates on a handful of large training efforts rather than spreading thin. The Opportunity You'll run controlled experiments on large models and let the data settle the arguments. You'll have the compute to test real hypotheses at scale, plus room to lead your own projects alongside the flagship runs. What You'll Do Research model architecture, training algorithms, optimisers and data pipelines Lead small projects independently while contributing to flagship training runs Design scaling-law experiments that set data, architecture and hyperparameter choices Scale training across thousands of accelerators with sound fault tolerance Build the dev tooling that keeps the research team moving quickly Work across the stack, from kernel throughput to high-level model design Design for downstream serving cost as well as model quality What You'll Bring Essential MS or PhD in CS, ML or a related field PyTorch distributed training, including FSDP and Megatron-style tensor or pipeline parallelism Debugging loss spikes, divergence and throughput regressions at scale Building and balancing data mixtures and tokenization pipelines Desirable Publications at NeurIPS, ICML, ICLR or similar venues FP8 or other low-precision training Custom CUDA or Triton kernels, or SSM-style and other non-standard architectures We encourage you to apply even if you don't meet every requirement. The right mindset matters as much as the right CV.   Apply Now If you want your experiments to shape the models themselves, we'd like to hear from you. Send your CV and we'll share the full brief in a confidential conversation.

https://www.gorovex.com/ Rovex Technologies Corporation Senior Autonomy Engineer - Robotics On-siteFull time Gainesville, Florida, United States OverviewApplication   Description About Rovex Rovex is building autonomous robots that help hospitals operate more efficiently so healthcare professionals can focus on what matters most: patient care. Our robots automate routine tasks such as patient transportation, logistics, and environmental monitoring, enabling healthcare systems to improve efficiency while enhancing the patient experience. We're a fast-growing robotics startup bringing together experts in robotics, AI, and healthcare to solve real-world problems. Our team works hands-on across hardware and software, rapidly iterating from prototype to deployment in close collaboration with hospital partners. If you're excited about building complex robotic systems, taking ownership, and seeing your work deployed in the real world, we'd love to hear from you. About the Role We are looking for a hands-on Senior Autonomous Robotics Engineer to help productionize and scale Rovex's autonomy stack as we deploy robotic fleets into dynamic, high-stakes hospital environments. This is a senior individual contributor and mentorship role. You'll work closely with our existing autonomy team to make key technical decisions, improve system architecture, and help build reliable, production-ready autonomy systems. Your work will bridge the gap between core software algorithms and physical execution, accelerating Rovex's path from early deployments to commercial scale. We are specifically looking for someone who has successfully deployed autonomous mobile robots in commercial environments at meaningful scale. The ideal candidate has helped bring 25+ autonomous robots into commercial operation and has firsthand experience solving the reliability, navigation, and operational challenges that emerge after robots leave the lab. Key Responsibilities Technical Leadership & Vision Help shape Rovex's autonomy roadmap, make key architectural decisions, mentor existing autonomy engineers, and guide the team toward reliable, production-ready systems. Production Autonomy Architecture Architect and deploy robust autonomy software that maximizes hardware performance while emphasizing reliability, maintainability, and observability in dynamic hospital environments. Complex Navigation & Edge Cases Solve challenging autonomy problems beyond basic point-to-point navigation, including crowded hallways, elevators, doorways, temporary obstacles, localization degradation, and other real-world deployment scenarios. Fleet Reliability & Diagnostics Develop logging, telemetry, health monitoring, and diagnostic tools that enable rapid failure analysis, remote support, and reliable fleet operations. Safety & Hardware Integration Design fault-tolerant autonomous systems and collaborate closely with software, electrical, mechanical, and systems engineers to ensure safe, dependable operation around patients and healthcare staff. Requirements Desired Education & Experience Bachelor's, Master's, or PhD in Robotics, Computer Science, Electrical Engineering, Computer Engineering, Mechatronics Engineering, or a related technical field (or equivalent industry experience). Typically 5+ years of professional experience developing autonomous robotic systems in industry. Experience deploying autonomous mobile robots in commercial environments, ideally including responsibility for fleets of 25+ robots operating successfully in the field. Demonstrated experience solving real-world autonomy challenges involving navigation, localization, reliability, and field debugging after deployment. Strong technical leadership skills with experience making architectural decisions while remaining a hands-on engineer. Passion for solving real-world robotics problems and building technology that directly improves hospital operations and patient care. Comfortable working in a fast-paced startup environment with a strong sense of ownership.   Technical Skills Production Robotics Proven experience building reliable autonomy systems and debugging complex field failures in commercial deployments. Autonomy Expertise Deep technical strength in one or more core autonomy disciplines such as localization, navigation, perception, planning, or safety, with practical experience deploying systems in real-world environments. Platforms & Tooling Strong proficiency in C++, Python, Linux, and ROS/ROS2, along with simulation and hardware-in-the-loop testing platforms. Reliability & Diagnostics Experience using logging, telemetry, and diagnostic tools to identify, reproduce, and resolve complex issues across deployed robotic fleets.   What We're Looking For Has successfully brought autonomous robots from development into commercial operation. Prioritizes shipping reliable, production-ready systems over impressive demonstrations. Builds maintainable, observable systems and avoids one-off fixes whenever possible. Comfortable working across software, sensors, controls, electrical, and mechanical systems to solve complex robotics challenges. Excellent communicator who enjoys mentoring teammates and collaborating across engineering disciplines. Excited by the opportunity to own difficult technical problems and help accelerate the commercialization of healthcare robotics. Benefits Base Salary: $140,000–$200,000, commensurate with experience. Equity: Eligible for stock options. Paid time off and company holidays. Opportunity to take technical ownership of mission-critical autonomy systems. Work alongside a world-class robotics team solving meaningful real-world healthcare challenges. Help shape the future of autonomous robotics in healthcare from an early-stage company.

Senior Data Mesh & Governance Consultant Contract | Fully Remote | Europe | Up to €850/day We are looking for a Senior Data Mesh & Governance Consultant to support a global data and AI transformation programme within a highly regulated environment. The project involves adapting an existing global data governance framework and data foundation to meet the needs of a specific business domain. Key requirements: 7+ years’ experience in data mesh, data governance or data architecture. Experience working across federated, global data environments. Strong understanding of enterprise data governance frameworks and data foundations. Familiarity with Databricks, Snowflake, AWS and Azure. Comfortable working with senior stakeholders and translating strategy into practical solutions. Previous experience in a technology consultancy or regulated environment is advantageous. Contract details: Fully remote across Europe, with Eastern Europe preferred. Full-time freelance contract through to the end of September. Up to €850/day, depending on location and experience. Immediate start, with availability before the 2 November kickoff preferred.