Senior Infrastructure Engineer London (Hybrid, 3 days/week in office) | Permanent | £110,000–£130,000 base + 15% bonus + benefits About the Company I’m supporting a confidential client developing AI software that helps protect people from telephone fraud. Its products apply voice AI to identify suspicious activity and help users make safer decisions during calls. The business is moving from product development into commercial deployment, working with telecommunications organizations. A small engineering team brings together backend and AI expertise, with this hire adding dedicated infrastructure ownership. About the Role As the first dedicated infrastructure engineer, you’ll establish the foundations for a managed production service and take responsibility for how it runs as adoption grows. The immediate priority is deployment, followed by improvements to reliability, performance, resource utilization, and cost. You’ll work closely with backend and ML engineers, alongside external engineering teams. This is a hands-on position with scope to shape infrastructure standards, make architectural decisions, and carry those decisions through to implementation. What You’ll Do Build and operate AWS infrastructure for production services, covering GPU inference and supporting CPU workloads. Own development and staging environments across AWS, with some Azure usage. Manage Kubernetes deployments through GitOps workflows using Helm and ArgoCD. Develop repeatable deployment, upgrade, and support processes for customer environments. Establish monitoring and alerting across internal environments and deployed production services. Improve capacity, performance, resource utilization, and infrastructure costs as usage increases. Create operational runbooks and incident response procedures, working with engineers to resolve reliability issues. What We’re Looking For Strong hands-on AWS experience, including independently establishing and operating infrastructure. Evidence of owning a production deployment and scaling it through an early period of growth. Practical Kubernetes experience and an understanding of reliable container orchestration. Experience with infrastructure as code, ideally Terraform, and GitOps deployment workflows. Experience monitoring production systems using Prometheus, Grafana, or comparable tools. The ability to investigate performance issues and make informed reliability and cost tradeoffs. Confidence working through incomplete requirements and taking responsibility for delivery in a small team. Nice to Have Experience with Azure alongside AWS. Kubernetes-based MLOps tooling, such as Kubeflow. Experience operating GPU workloads, including drivers, scheduling, and utilization. Familiarity with AI engineering, voice applications, or telecommunications systems. Experience deploying into air-gapped customer environments. Knowledge of secrets management and least-privilege access across environments. Why Join Infrastructure ownership. Establish the technical foundations and operating practices as the first dedicated infrastructure hire. Visible impact. Support a commercial launch and see how your decisions affect service reliability and growth. Meaningful application. Build systems behind technology designed to help protect people from fraud. Broad technical scope. Work across cloud infrastructure, GPU services, deployment automation, and production operations. Close collaboration. Work directly with backend and ML engineers, with room to influence technical direction. What to Expect This is a small team moving toward commercial delivery, so priorities will evolve and some problems will arrive without a complete specification. You’ll need to be comfortable making practical decisions and implementing them with limited existing infrastructure support. The role is based in London, with three office days each week. Previous AI or telecommunications experience is useful but is not required. The package includes a £110,000–£130,000 base salary, a 15% bonus, private medical cover, life insurance, an 8% matched pension contribution, subsidized food delivery on office days, and office refreshments. Candidates will need an existing right to work in the UK, as sponsorship is not expected to be available for this appointment. Interview Process The expected process is: An initial phone screening conversation. A second phone screening conversation. An on-site technical interview covering technical questions and hypothetical scenarios. An on-site conversation about working style and team fit, with an opportunity to meet the wider team. Get in touch with Curtis Holben at DeepRec.ai for a confidential conversation.

AI & Digital Solutions Engineer Germany | Permanent, full time | Hybrid working Build practical AI solutions for manufacturing An established German industrial group is creating a new central role to bring software, automation and AI into its engineering, production and business processes. The group has more than 1,000 employees across seven production sites. Its businesses work across industrial production, energy, engineering and transport. AI adoption is at an early stage, giving you the opportunity to identify where technology can improve day to day operations and deliver solutions from the first idea through to production. This is a hands on role for a software developer or AI engineer who understands that the right answer may be a process change, a software application, automation or AI. What you will do • Analyse processes across engineering, production, maintenance, quality, purchasing and administration. • Find and prioritise opportunities for automation and AI. • Build applications using LLMs, RAG, enterprise search and AI agents. • Connect ERP, DMS, production, engineering and Microsoft 365 systems through APIs and integrations. • Develop custom applications, services and automations where existing software does not meet the need. • Evaluate cloud, on premises and open source models, including Microsoft Copilot where suitable. • Help build and operate AI infrastructure, development environments, monitoring and logging. • Take successful proofs of concept into reliable production systems. • Work directly with employees across production, engineering and administration to understand how work happens and where time is lost. Example projects You could help make engineering knowledge easier to find and use, analyse technical documents and drawings, support development and costing, or connect production data with simulation results. Other projects could include linking knowledge about custom tooling, machine manuals, wiring diagrams, fault codes, maintenance history and spare parts. You could also automate recurring ERP and administrative processes. Every solution must account for data privacy, information security and access permissions. What you will bring • Strong practical software development experience, particularly with Python. • Experience building and integrating APIs, SQL databases and business systems. • Practical knowledge of LLMs and generative AI. • Experience with RAG, embeddings, vector databases, AI agents or tool calling. • Confidence taking an application from an early idea or proof of concept into production. • An understanding of manufacturing or production environments gained through relevant work in a manufacturing company. • A practical approach to investigating processes and choosing the right technical solution. • German at B2 level or higher. Native or near native German is preferred because the role involves working directly with production teams and reading German documentation. Experience with C# or .NET, Java, TypeScript, Linux, Docker, ERP, DMS, MES, Microsoft 365, Azure, CAD, CAE or industrial data would be useful. What is offered • A newly created role with room to shape the group’s AI and software architecture. • Direct collaboration with senior management, IT and business teams. • Work across real production, engineering and business processes. • The opportunity to take solutions from process analysis through to production. • Permanent full time employment. • Up to two days working from home each week, with regular visits to production locations. • An Edenred benefits card with a welcome credit and additional credits during the year. The role can be based in Germany. St. Ingbert, Baiersdorf and Forchheim are being considered. A preference for St. Ingbert was expressed during the hiring discussion because of its proximity to a major production site. The written job description lists Baiersdorf as preferred, so the preferred base should be confirmed. Interested in building useful AI solutions inside a real manufacturing environment? Get in touch to discuss the role.

Machine Learning Engineer – Inference Optimization Overview We are looking for a Machine Learning Engineer focused on low-latency inference optimization to help build, tune, and productionize high-performance model serving systems. This role sits at the intersection of machine learning, systems engineering, and GPU performance. You will work on inference workloads where latency, throughput, reliability, and hardware efficiency all matter, and where a deep understanding of modern inference runtimes can meaningfully improve production outcomes. You will work closely with researchers and engineers to understand model structure, identify inference bottlenecks, and turn research ideas into efficient production systems. The work may involve other types of models, but focuses on transformer-style architectures and structured inference workloads. You will evaluate and tune frameworks and related serving or compilation systems, while also reasoning about GPU execution, memory layout, batching strategies, precision tradeoffs, and end-to-end latency. What you'll do: Design, build, and optimize low-latency inference systems for production machine learning workloads. Profile model inference pipelines across model execution, runtime configuration, batching, memory movement, serialization, networking, and I/O. Evaluate, integrate, and tune inference runtime systems. Improve latency, throughput, and GPU utilization for production inference workloads. Build and support benchmarking and profiling tools to compare model variants, hardware targets, runtime configurations, and deployment strategies. Debug performance issues involving GPU memory, compute saturation, kernel behavior, CPU/GPU coordination, data movement, and serving-layer overhead. Help shape model and system design choices so that research models are efficient to deploy under real latency constraints. Where necessary, collaborate with lower-level systems or GPU specialists on custom operators, kernel-level optimization, or hardware-specific performance work. What we're looking for: Experience deploying, optimizing, or operating machine learning inference workloads in production or production-like environments. Programming experience in Python, Java, C# etc. and at least one systems language such as C, C++, Rust, or Go. Solid understanding of modern ML frameworks such as PyTorch, including model execution, export, tracing, compilation, and performance profiling. Ability to reason about latency, throughput, batching, memory use, GPU utilization, and reliability under real workloads. Strong practical judgment around tradeoffs between model quality, latency, throughput, implementation complexity, and maintainability. Preferred qualifications: Experience optimizing inference for latency-sensitive or high-throughput applications. Experience with model optimization techniques such as quantization, pruning, distillation, operator fusion, graph lowering, custom operators, or model compilation. Exposure to CUDA, Triton language, ROCm, PTX, CuTe, CUTLASS, FlashInfer, or similar low-level GPU programming tools. Experience running inference workloads on Kubernetes or GPU clusters, including scheduling, autoscaling, observability, and resource management. Background in mathematics, physics, computer science, engineering, statistics, or another technical field. Demonstrated ability to improve real-world inference performance beyond a baseline framework implementation.

Summary An opportunity to join an innovative quantum computing company developing photonic quantum systems for machine learning, generative AI and optimisation. You will develop and implement practical quantum machine learning and optimisation applications, working with customers, research partners and internal engineering teams to take projects from concept through to implementation and benchmarking. This is a hands-on role combining quantum algorithm development, scientific programming and technical collaboration. Key Responsibilities Develop and implement quantum machine learning and optimisation applications for real-world use cases. Design, prototype and benchmark algorithms using the company's quantum hardware and software. Collaborate with customers, research partners and internal engineering teams on technical projects. Evaluate application performance and communicate findings through reports, presentations and publications. Contribute improvements to software tools, algorithms and libraries. Support technical discovery calls and proof-of-concept projects alongside commercial teams. Deliver technical workshops, customer training and onboarding. Document applications and methodologies to support knowledge sharing. Requirements Essential: Master's or PhD in Physics, Computer Science or a related field. Experience in quantum machine learning and/or quantum optimisation. Strong scientific programming skills, particularly Python and Git. Experience with relevant quantum computing and ML frameworks. Ability to independently scope and deliver complex technical projects. Experience collaborating with external technical partners or end-users. Excellent communication skills and the ability to explain complex concepts to different audiences. Desirable: Experience with photonic quantum computing. Quantum hardware and application benchmarking. Hybrid quantum-classical algorithms and architectures. Strong classical machine learning expertise. Publications in quantum computing or ML. Previous customer-facing, technical consulting or pre-sales experience. Benefits & Opportunity Work at the forefront of commercial quantum computing and AI. Hands-on involvement in developing real-world quantum applications. Exposure to advanced photonic quantum hardware and hybrid architectures. Collaborate with leading customers, research partners and engineering teams. Influence software development, applications and technical roadmaps. Competitive salary and benefits package. Hybrid working in Central London.

Machine Learning Engineer, Speech LLMs   You’d be an early member of a new speech LLM lab inside a profitable, bootstrapped company, with the job of training models that understand and generate speech natively.   About the company A profitable Voice AI company based in San Francisco. It builds hardware and software that capture what professionals say and hear, then turn it into notes and usable information. The business has a large global user base and is now setting up a core SpeechLLM lab, which this person would join as a founding hire.   The role You’ll build and train large-scale audio and speech models from the ground up. That could mean unified SpeechLLMs, advanced ASR, expressive TTS or generative audio architectures. One day you’re designing a new sequence modelling architecture, the next you’re debugging a distributed training cluster. The work runs across the whole stack, from signal processing and raw acoustic representations to foundation model training and optimisation for edge devices. You’ll own ambiguous problems and take them into production.   What we’re looking for A proven track record of building and training large-scale audio or speech models. Deep expertise in PyTorch or JAX, including optimising large distributed training runs, managing GPU memory and fixing performance bottlenecks. Comfort moving between research and engineering, and a real interest in AI systems that handle speech natively.   Any of the following is a bonus: Text LLM pretraining, instruction tuning or RLHF Neural audio codecs for streamable, high-fidelity audio Diffusion, flow matching or autoregressive models for speech and voice generation RL techniques such as RLHF or GRPO to improve conversational cadence, steerability and alignment Inference optimisation with serving frameworks like vLLM, TensorRT-LLM or SGLang Large GPU clusters, distributed training with FSDP or DeepSpeed, and Kubernetes   Salary and benefits Up to $270K, plus a performance bonus and equity. Healthcare for you and your dependents, including dental and vision, with a generous employer subsidy. 401(k) with company matching. Unlimited PTO plus 13 paid holidays.   Logistics Full-time, permanent. Based in San Francisco, hybrid, with a minimum of three days a week in the office.

Agentic RL researcher, post-training (San Francisco or remote)   About the company A well-funded voice AI company in San Francisco, building speech models after a recent raise. The research team is growing, with several researcher roles open at once, and is kept deliberately flat. This role reports to the Head of AI.   The role You’ll own the RL side of post-training the next text-to-speech model. A big part of how that model gets good is how it’s post-trained, which means building complex, text-based agent loop systems that generate the training signal for RL. You’ll design and run PPO, GRPO, DPO or on-policy loops on model weights, with the environments and rewards that go with them. You’ll also build pipelines that write their own questions and tasks, so training scales without constant manual data curation. Ablations show what moved behaviour, and you’ll work with the speech researchers so what you build in text carries over to audio.   What we’re looking for Hands-on RL or preference learning that trained model weights and changed behaviour in a way you can measure. You’ve built or led an RL pipeline that scales, ideally one that generates its own questions and tasks (curating an SFT dataset on its own isn’t what this role needs). You’ve done it at a real company, with real constraints, on chatbot-style or conversational agents. Bonus: audio or speech post-training, large-scale distributed training, and published or open-source work in LLM post-training.   Salary and benefits Up to around $250k depending on experience, plus equity. Healthcare, dental and vision.   Logistics Full-time, permanent. San Francisco hybrid or fully remote. Start date ASAP.

Machine Learning Researcher, Audio (TTS) The text-to-speech model already works really well. The job is the last 10% of post-training: the pronunciation errors and small issues that only show up live.   About the company A well-funded voice AI company in San Francisco that builds AI phone agents for enterprise customers and now trains its own speech models. The research team has just finished evaluating its newest model and is moving into full development. Research headcount is growing, and text-to-speech is now the main hiring priority.   The role You’ll own the text-to-speech model from research prototype to production. That means post-training it until it’s reliable and stable, fixing mispronunciations and the issues that only appear live, and building on neural audio codecs and discrete audio representations.   You’ll ship into a low-latency, telephony-class path and work alongside the speech-to-speech and LLM researchers. You report to the Head of AI in a deliberately flat team, with a possible technical lead path for the right person.   What we’re looking for Hands-on ownership of a neural audio codec or audio tokenizer, from training objective through to evaluation. Experience training generative audio models with a clear personal contribution to the architecture and training, beyond data, evals or product glue. Real industry experience taking a TTS model from research prototype to production scale, plus post-training work that made it reliable and stable. You can ship as well as publish. Shipping into a low-latency telephony path, ASR experience alongside TTS, and first or second author papers in non-generic areas are a bonus.   Salary and benefits Up to around $300K depending on experience, plus equity. Healthcare, including dental and vision.   Logistics San Francisco office hybrid or fully remote. Start as soon as possible.

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.