Member of Technical Staff

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.

ABOUT BLACK FOREST LABS We’re the team behind Latent Diffusion, Stable Diffusion, and FLUX—foundational technologies that changed how the world creates images and video. We’re creating the generative models that power how people make images and video—tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we’re just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we’re scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. WHY THIS ROLE You'll train large-scale diffusion models for image and video generation, exploring new approaches while maintaining the rigor that helps us distinguish meaningful progress from incremental tweaks. This isn't about following established recipes—it's about running the experiments that clarify which architectural choices matter and which are less impactful. WHAT YOU’LL WORK ON * Trains large-scale diffusion transformer models for image and video data, working at the scale where intuitions break and empirical evidence matters * Rigorously ablates design choices—running experiments that isolate variables, control for confounds, and produce insights you can actually trust—then communicating those results to shape our research direction * Reasons about the speed-quality tradeoffs of neural network architectures in production settings where both constraints matter simultaneously * Fine-tunes diffusion models for specialized applications like image and video upscalers, inpainting/outpainting models, and other tasks where general-purpose models aren't enough WHAT WE’RE LOOKING FOR You've trained large-scale diffusion models and developed strong intuitions about what matters. You know that at research scale, every design choice has tradeoffs, and the only way to know which ones are worth making is through careful ablation. You're comfortable debugging distributed training issues and presenting research findings to the team. You likely have: * Hands-on experience training large-scale diffusion models for image and video data, with practical knowledge of common failure modes and what matters most in training * Experience fine-tuning diffusion models for specialized applications—upscalers, inpainting, outpainting, or other tasks where understanding the domain matters as much as understanding the architecture * Deep understanding of how to effectively evaluate image and video generative models—knowing which metrics correlate with quality and which are just convenient proxies * Strong proficiency in PyTorch, transformer architectures, and the full ecosystem of modern deep learning * Solid understanding of distributed training techniques—FSDP, low precision training, model parallelism—because our models don't fit on one GPU and training decisions impact research outcomes We'd be especially excited if you: * Have experience writing forward and backward Triton kernels and ensuring their correctness while considering floating point errors * Bring proficiency with profiling, debugging, and optimizing single and multi-GPU operations using tools like Nsight or stack trace viewers * Know the performance characteristics of different architectural choices at scale * Have published research that contributed to how people think about generative models HOW WE WORK TOGETHER We’re a distributed team with real offices that people actually use. Depending on your role, you’ll either join us in Freiburg or SF at least 2 days a week (or one full week every other week), or work remotely with a monthly in-person week to stay connected. We’ll cover reasonable travel costs to make this possible. We think in-person time matters, and we’ve structured things to make it accessible to all. We’ll discuss what this will look like for the role during our interview process. Everything we do is grounded in four values: * Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful. * Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task. * Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect. * Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos. If this sounds like work you’d enjoy, we’d love to hear from you. Base Annual Salary: EU €130,000-€340,000 + Equity