Sqwish
Cambridge, Cambridgeshire
About Sqwish Labs . At Sqwish Labs we build the infrastructure that lets AI products learn from real production outcomes, so signals like customer resolution, conversion, trust, time saved, safety and cost can shape what the system does next. The work is hard in the best way. Part of it is research: learning from messy, delayed, real-world feedback instead of clean benchmarks. Part of it is engineering: building reliable infrastructure that can sit inside live AI systems and make good decisions request by request. We're a small, ambitious team with deep Cambridge roots, building with flexibility as we grow. We care deeply about what we build and how we build it. We move quickly, ask sharp questions, learn out loud, and try to stay honest about what reality is teaching us. No one has all the answers here, and that's part of what makes it exciting. Role Focus This role focuses on helping the founders turn high-priority company problems into structured action. You will work across GTM, operations, strategy, hiring, customer coordination, and investor preparation, with a strong emphasis on clear thinking and follow-through. The work combines market understanding, crisp communication, process design, and ownership of projects that keep a fast-moving AI infrastructure company aligned and moving. The right person will be highly organised, commercially sharp, and excited by ambiguity. You understand, or are hungry to understand, the AI ecosystem. You might come from GTM, venture, consulting, startup operations, product, or a technical AI background. You are the kind of person who can take a vague problem, identify what matters, create structure, and drive it to a useful outcome without needing everything explained twice. The problems you'll tackle Keeping high-priority work moving across a small, fast-moving technical team Helping the founders turn strategic, commercial, and operational questions into clear action Researching AI markets, customers, competitors and design partners Supporting GTM experiments across outbound, partnerships, customer discovery, and founder-led sales Creating lightweight operating systems for hiring, fundraising, customer follow-up, finance, and internal coordination Structuring ambiguous information into decision memos, dashboards and executive updates Core responsibilities Work directly with the founders on high-leverage company priorities Run market, customer, competitor, and investor research with clear written outputs Support customer discovery, design-partner coordination, GTM operations, and follow-ups Prepare founder updates, investor materials, internal memos, and operating documents Own special projects from ambiguous brief to finished outcome Build and maintain lightweight systems in Notion, spreadsheets, CRM and docs Spot bottlenecks, clarify owners, set deadlines, and make sure important work does not disappear We don't expect mastery of every bullet, strength in some plus the drive to learn the rest beats a perfect checklist. Nice to have, but teachable on the job Experience in AI, SaaS, venture capital, consulting, GTM, product, or startup operations Strong understanding of, or curiosity about, LLMs, AI infrastructure, developer tools, and enterprise AI adoption Excellent writing: concise memos, clear updates, structured research, and strong synthesis Comfort with spreadsheets, Notion, CRM tools, dashboards, and basic data analysis Ability to manage multiple workstreams without losing the thread Experience supporting founders, executives, investors, sales teams, or technical teams Technical literacy, coding experience, or an AI/ML background is a plus but not required What to expect: £40k-£60k (+equity if applicable) based on location and experience
About Sqwish Labs . At Sqwish Labs we build the infrastructure that lets AI products learn from real production outcomes, so signals like customer resolution, conversion, trust, time saved, safety and cost can shape what the system does next. The work is hard in the best way. Part of it is research: learning from messy, delayed, real-world feedback instead of clean benchmarks. Part of it is engineering: building reliable infrastructure that can sit inside live AI systems and make good decisions request by request. We're a small, ambitious team with deep Cambridge roots, building with flexibility as we grow. We care deeply about what we build and how we build it. We move quickly, ask sharp questions, learn out loud, and try to stay honest about what reality is teaching us. No one has all the answers here, and that's part of what makes it exciting. Role Focus This role focuses on helping the founders turn high-priority company problems into structured action. You will work across GTM, operations, strategy, hiring, customer coordination, and investor preparation, with a strong emphasis on clear thinking and follow-through. The work combines market understanding, crisp communication, process design, and ownership of projects that keep a fast-moving AI infrastructure company aligned and moving. The right person will be highly organised, commercially sharp, and excited by ambiguity. You understand, or are hungry to understand, the AI ecosystem. You might come from GTM, venture, consulting, startup operations, product, or a technical AI background. You are the kind of person who can take a vague problem, identify what matters, create structure, and drive it to a useful outcome without needing everything explained twice. The problems you'll tackle Keeping high-priority work moving across a small, fast-moving technical team Helping the founders turn strategic, commercial, and operational questions into clear action Researching AI markets, customers, competitors and design partners Supporting GTM experiments across outbound, partnerships, customer discovery, and founder-led sales Creating lightweight operating systems for hiring, fundraising, customer follow-up, finance, and internal coordination Structuring ambiguous information into decision memos, dashboards and executive updates Core responsibilities Work directly with the founders on high-leverage company priorities Run market, customer, competitor, and investor research with clear written outputs Support customer discovery, design-partner coordination, GTM operations, and follow-ups Prepare founder updates, investor materials, internal memos, and operating documents Own special projects from ambiguous brief to finished outcome Build and maintain lightweight systems in Notion, spreadsheets, CRM and docs Spot bottlenecks, clarify owners, set deadlines, and make sure important work does not disappear We don't expect mastery of every bullet, strength in some plus the drive to learn the rest beats a perfect checklist. Nice to have, but teachable on the job Experience in AI, SaaS, venture capital, consulting, GTM, product, or startup operations Strong understanding of, or curiosity about, LLMs, AI infrastructure, developer tools, and enterprise AI adoption Excellent writing: concise memos, clear updates, structured research, and strong synthesis Comfort with spreadsheets, Notion, CRM tools, dashboards, and basic data analysis Ability to manage multiple workstreams without losing the thread Experience supporting founders, executives, investors, sales teams, or technical teams Technical literacy, coding experience, or an AI/ML background is a plus but not required What to expect: £40k-£60k (+equity if applicable) based on location and experience
Sqwish
Cambridge, Cambridgeshire
£40k-£90k depending on location and experience About Sqwish Labs. At Sqwish Labs we build the infrastructure that lets AI products learn from real production outcomes, so signals like customer resolution, conversion, trust, time saved, safety and cost can shape what the system does next. The work is hard in the best way. Part of it is research: learning from messy, delayed, real-world feedback instead of clean benchmarks. Part of it is engineering: building reliable infrastructure that can sit inside live AI systems and make good decisions request by request. We're a small, ambitious team with deep Cambridge roots, building with flexibility as we grow. We care deeply about what we build and how we build it. We move quickly, ask sharp questions, learn out loud, and try to stay honest about what reality is teaching us. No one has all the answers here, and that's part of what makes it exciting. This role focuses on building the product infrastructure behind closed-loop AI: the APIs, services, data models, and operational systems that let Sqwish learn from real outcomes. You will work across low-latency serving paths, feedback and metrics pipelines, background workers, reliability, and observability. The work combines strong backend engineering with enough ML infrastructure context to build systems that research can trust and production can depend on. The right person will be excited by complex, high-leverage engineering+ML problems where correctness, latency, reliability, and product constraints all matter at once. You enjoy building systems that are clean enough to reason about, but pragmatic enough to ship. You care about tests, observability, and operational safety, but you do not hide behind process. Problems you'll tackle Building low-latency optimisation APIs that sit on the critical path of AI products Capturing decisions, model outputs, feedback, cost, latency, and outcome signals reliably Designing backend systems that support continuous learning from production data Building Rust and Python services across serving, workers, training workflows, and internal tooling Working with Postgres, Redis, queues/streams, migrations, and event-driven workflows Making reliability, observability, and deployment safety part of the product from the beginning Core responsibilities Write production-grade Rust and Python services Design clean domain boundaries around requests, functions, variants, metrics, rewards, and feedback Build APIs and data flows that keep serving, reporting, and learning paths consistent Own database schemas, migrations, background jobs, and operational guardrails Instrument systems with structured logs, traces, metrics, dashboards, and useful alerts Improve local development, CI, Docker, E2E tests, and release workflows Work closely with research, product, and design to turn ambiguous system problems into shippable infrastructure We don't expect mastery of every bullet, strength in some plus the drive to learn the rest beats a perfect checklist. Nice to have, but teachable on the job Some experience or interest in ML Experience with Rust, Axum, Tokio, SQLx, or PyO3 Experience with FastAPI, Pydantic, SQLAlchemy, Alembic, pytest, mypy, or Ruff Familiarity with Postgres, Redis, event-driven systems, queues, or streaming architectures Experience with OpenTelemetry, Prometheus, Grafana, Loki, Tempo, or structured logging Comfort with Docker, Kubernetes, Helm, Terraform, GitHub Actions, or release automation Exposure to LLM infrastructure, model routing, embeddings, GPU workers, or ML platform systems Experience with SOC 2 / ISO 27001 readiness, audit trails, secrets handling, or production compliance What to expect: £40k-£90k (+equity) based on location and experience Location: Cambridge in person (preferred) but remote available too. We can sponsor UK visas.
£40k-£90k depending on location and experience About Sqwish Labs. At Sqwish Labs we build the infrastructure that lets AI products learn from real production outcomes, so signals like customer resolution, conversion, trust, time saved, safety and cost can shape what the system does next. The work is hard in the best way. Part of it is research: learning from messy, delayed, real-world feedback instead of clean benchmarks. Part of it is engineering: building reliable infrastructure that can sit inside live AI systems and make good decisions request by request. We're a small, ambitious team with deep Cambridge roots, building with flexibility as we grow. We care deeply about what we build and how we build it. We move quickly, ask sharp questions, learn out loud, and try to stay honest about what reality is teaching us. No one has all the answers here, and that's part of what makes it exciting. This role focuses on building the product infrastructure behind closed-loop AI: the APIs, services, data models, and operational systems that let Sqwish learn from real outcomes. You will work across low-latency serving paths, feedback and metrics pipelines, background workers, reliability, and observability. The work combines strong backend engineering with enough ML infrastructure context to build systems that research can trust and production can depend on. The right person will be excited by complex, high-leverage engineering+ML problems where correctness, latency, reliability, and product constraints all matter at once. You enjoy building systems that are clean enough to reason about, but pragmatic enough to ship. You care about tests, observability, and operational safety, but you do not hide behind process. Problems you'll tackle Building low-latency optimisation APIs that sit on the critical path of AI products Capturing decisions, model outputs, feedback, cost, latency, and outcome signals reliably Designing backend systems that support continuous learning from production data Building Rust and Python services across serving, workers, training workflows, and internal tooling Working with Postgres, Redis, queues/streams, migrations, and event-driven workflows Making reliability, observability, and deployment safety part of the product from the beginning Core responsibilities Write production-grade Rust and Python services Design clean domain boundaries around requests, functions, variants, metrics, rewards, and feedback Build APIs and data flows that keep serving, reporting, and learning paths consistent Own database schemas, migrations, background jobs, and operational guardrails Instrument systems with structured logs, traces, metrics, dashboards, and useful alerts Improve local development, CI, Docker, E2E tests, and release workflows Work closely with research, product, and design to turn ambiguous system problems into shippable infrastructure We don't expect mastery of every bullet, strength in some plus the drive to learn the rest beats a perfect checklist. Nice to have, but teachable on the job Some experience or interest in ML Experience with Rust, Axum, Tokio, SQLx, or PyO3 Experience with FastAPI, Pydantic, SQLAlchemy, Alembic, pytest, mypy, or Ruff Familiarity with Postgres, Redis, event-driven systems, queues, or streaming architectures Experience with OpenTelemetry, Prometheus, Grafana, Loki, Tempo, or structured logging Comfort with Docker, Kubernetes, Helm, Terraform, GitHub Actions, or release automation Exposure to LLM infrastructure, model routing, embeddings, GPU workers, or ML platform systems Experience with SOC 2 / ISO 27001 readiness, audit trails, secrets handling, or production compliance What to expect: £40k-£90k (+equity) based on location and experience Location: Cambridge in person (preferred) but remote available too. We can sponsor UK visas.