Member of Technical Staff - Software Engineer

xAI

SpaceXAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. ABOUT THE ROLE: We are seeking a talented Software Engineer to join our X Money team, focused on building a revolutionary global payment network that will serve over 600 million users and rival the world’s largest financial institutions. In this role, you will specialize in backend development, designing and optimizing robust microservices to ensure scalability, security, and reliability. You will support full-stack efforts, collaborate with cross-functional teams on payments, fraud detection, and compliance initiatives, and contribute to the creation of a high-scale financial products platform. This is an opportunity to work on greenfield projects in a fast-paced, startup-like environment, driving innovation at the intersection of AI and finance. RESPONSIBILITIES: * Develop backend services, APIs, and data models to support high-volume, multi-user environments. * Work with iOS, Android & Web client engineers to ship products. * Design robust infrastructure and microservices for payments, transactions, growth, monetization, and engagement across platforms. * Build and maintain fullstack features, including user dashboards, personalized experiences, content delivery, interactive tools, assessments, and real-time analytics.  * Lead architecture, scalability, and reliability decisions for high-concurrency, low-latency systems. * Uphold engineering excellence via testing, monitoring, deployment, and secure data handling. BASIC QUALIFICATIONS: * Proficiency in distributed systems for high-scale, low-latency environments; languages like Rust, Go, Python & Java, and high volume streaming systems. * 2+ years of experience working on large scale consumer applications. PREFERRED SKILLS AND EXPERIENCE: * 5+ years of experience working on large scale consumer applications or early-mid stage startup experience as a founding engineer, emphasizing rapid prototyping, user-centric design, and AI solutions. COMPENSATION AND BENEFITS: £107,000 - £262,000 GBP Base salary is just one part of our total rewards package at SpaceXAI, which also includes equity, comprehensive healthcare and dental coverage, cash plan, income protection, life insurance, and various other discounts and perks. SpaceXAI is an equal opportunity employer. For details on data processing, view our Recruitment Privacy Notice.

Gradient Labs

AT GRADIENT LABS, WE'RE BUILDING THE AI CUSTOMER OPERATIONS PLATFORM FOR FINANCIAL SERVICES. Founded in 2023, we now work with some of the biggest names in banking and fintech. Our platform runs specialist agents, purpose-built for financial services, to eliminate manual work across customer support and back-office operations. Together, they give product and operations teams the visibility and control to trust every outcome. We're a team of builders from companies like Monzo, Wise, Mastercard, Revolut and Google. If you're excited to tackle some of the hardest problems in AI and help shape the future of customer operations, we'd love to hear from you. 🎯 HOW YOU’LL MAKE AN IMPACT This is a build-and-ship role. You'll turn ambiguous customer support problems into reliable, observable AI agents that handle live conversations for real users. You'll work close to production — designing prompts and tool flows, building eval suites, shipping changes, watching what breaks, and iterating fast. * Build and operate AI agents in production: Design, implement, and maintain agentic systems powered by LLMs — handling tool calling, multi-step reasoning, and integration with customer APIs and data sources. You'll own these systems end-to-end: reliable, observable, and auditable from day one. * Translate business problems into agentic workflows: Work directly with enterprise customers to understand their workflows, surface the highest-leverage automation opportunities, and frame them as well-scoped agent problems with clear success criteria. You'll be the technical counterpart in customer conversations, turning ambiguity into a concrete plan. * Build robust evaluation infrastructure: Create and maintain eval suites drawn from real-world scenarios and edge cases. Go beyond vibes-based testing: structured evals measuring accuracy, safety, and latency, tied to clear business outcomes, used to drive systematic improvements to prompts, tools, and behaviour. * Enhance our agent: Develop, evaluate, and optimise the skills that make up our agent. Curate datasets, iterate on improvements, test changes, and ship successful approaches into production. * Shape our internal AI platform: Contribute to shared libraries, patterns, and standards for how we build, evaluate, and deploy agents across customers. Help define how we approach prompting, tool orchestration, retrieval, and monitoring. * Experiment and prototype: Keep up with the latest in NLP, agentic systems, and generative AI. Prototype against our hardest problems with a bias toward shipping experiments quickly rather than long research cycles. * Analyse data: Work across customer queries, support tickets, and related data to find patterns and identify what our agents could automate next. * Drive technical decisions: Scope your own work, push back when the framing is wrong, and tell us when the plan needs to change. 💡 WHAT YOU’LL BRING * 4+ years of professional software engineering experience, with a meaningful focus on Machine Learning, NLP, or applied AI. * End-to-end ownership of production systems: you've designed it, shipped it, watched it break, and iterated. You can point to specific moments where you drove a decision forward. * Hands-on experience shipping LLM-based features or agent systems into production, not just prototypes. Go experience a plus. * Strong Python skills: clean, testable, observable production-grade code. * You're always automating. Your default reaction to a repetitive task is to build something for it. * Comfort reasoning about trade-offs (accuracy vs latency, coverage vs precision, cost vs quality), framed around what matters to the user and the business. * A product-driven posture: you've worked closely with PMs, customers, or domain experts, and translated ambiguity into shipped solutions without needing a perfect spec. * A preference for fast iteration over long research cycles. WHY JOIN GRADIENT LABS? This is a unique chance to be part of a team working with cutting-edge technology to reshape how businesses will operate in the future. Over the next 10 years, every company will need to embrace AI-powered operations to stay competitive, and this role puts you right in the middle of that transformation. You’ll tackle challenging and new problems, work with some of the most exciting brands across different industries, and be surrounded by a passionate, smart team that’s driven to build something groundbreaking.

Tessl

Tessl is a fast-growing Series A startup based in London, founded by Guy Podjarny. We’ve raised over $100M from world-class investors including Index Ventures, Accel, GV, and Boldstart, and in 2025 we were ranked #2 in Sifted EU’s B2B SaaS Rising 100 and #20 in Sifted's AI 100. At Tessl, we are building the context layer for AI coding agents, and a platform for AI-native software development. As an early member of the team, you’ll help shape how we build, scale and support a company operating at the edge of AI and software development. OVERVIEW OF THE ROLE We're hiring a Research Engineer to join our AI Research (AIR) team. You'll work on the components that make the outer loop real: how agent harnesses orchestrate model behaviour, how we evaluate what's actually working, how pipelines turn production traces into the next round of improvement, and how we diagnose the failure modes that matter to real users. These aren't four separate workstreams — they're parts of one system, and we want people who see them that way. We expect you to sit close to customers — joining calls, watching sessions, reading traces — and to let real workflows shape your research priorities. You'll have meaningful autonomy and the resources to run substantial experiments where the bar for success is shipped impact. You'll report to our AI Research Lead, and collaborate closely with engineering, product, and design. WHAT WE'RE LOOKING FOR We're explicitly building coverage across four skill areas. You don't need to be strong in all of them — but you should bring depth in at least one: * Agent harness and orchestration design — how tools, context, and control flow combine to make a useful agent. * Agentic eval methodology — task and repo-level evals, dataset curation, the craft of measuring what actually matters. * Outer-loop and pipeline thinking — feedback loops, training-data flywheels, bandit-style optimisation, anything that goes beyond a single agent session. * Failure-mode analysis — instrumenting agents, reading traces at volume, surfacing patterns engineering can act on. ESSENTIAL * 4+ years shipping AI/ML products in a startup or applied industry setting, with recent hands-on experience with LLMs and agentic systems. * Demonstrated depth in at least one of the four skill areas above. * Strong product and customer instincts: comfort joining customer calls, watching session recordings, and letting real workflows shape what you work on. * Sharp evaluation judgement: benchmarks where they exist, vibes and quick prototypes where they don't, and the taste to know which is appropriate. * Experience building datasets for evaluation or training, including the pipeline work that goes with it. * Deeply curious about agents and excited about reshaping how software is built. NICE TO HAVE * A Masters or PhD in a relevant computational field. * Direct experience with coding agents or code-generation systems. * Background in RL, bandits, or other outer-loop optimisation frameworks applied to LLMs. * Experience building synthetic data, dataset infrastructure, or internal tooling that other engineers actually used. * A project you can show us (GitHub links welcome) and a thoughtful answer to "Why Tessl?" WHAT YOU'LL DO No two weeks will look the same. A flavour: * Sit in on a customer session, understand how their agents are failing, design an eval that captures it, and drive a fix through to shipped improvement. * Close a piece of the outer loop end to end: production signal in, dataset out, eval scored, harness change shipped, metric moved. * Own a slice of our eval infrastructure: dataset curation, harness configuration, runner, analysis, and the comms back to engineering. * Prototype a new harness or context configuration and measure whether it actually moves the needle on real customer tasks. * Dig through pages of agent traces, build the tooling you need to make sense of them, and brief the team on what you found. * Partner with product and engineering on near-term shipping problems by bringing research rigour. * Pull a recent paper apart, work out what's actually transferable to our platform, and turn it into a concrete experiment. YOU’LL BE SUCCESSFUL IF… In your first 3 months, you might have shipped a new eval suite for a real customer workflow, improved an agent harness based on trace analysis, or built a pipeline that turns production failures into reusable test cases. SALARY AND BENEFITS Competitive salary commensurate with experience. Health insurance extending to partners and dependents, pension contributions, and the rest of what you'd expect. Our office is a couple of minutes from King's Cross — pet friendly, with regular team lunches, drinks, and socials. We're hybrid, with Monday, Tuesday, and Thursday as the primary in-office days. APPLICATION PROCESS * Intro call to understand "Why Tessl?" and to tell you a bit about us. * A call with our AI Research Lead to understand your ways of working and how you use agents. * A 4 hour technical take-home exercise extending our one-shot implementation. * A half-day on-site session including whiteboarding and hands-on activities. * Leadership chats with our Head of People, Head of Engineering and CEO. We care deeply about the warm, inclusive environment we’re building at Tessl and we value diversity – we welcome applications from those typically underrepresented in tech. If you like the sound of this role but are not totally sure whether you’re the right person, do apply anyway! LEARN HOW WE THINK AND WORK * On Tessl, The AI Native Development Startup * Announcing skills on Tessl: the package manager for agent skills * Podcast Episode: The End of Fragmented Agent Context, Guy Podjarny Tessl CEO