Applied AI Engineer

Hey! We're team Granola 👋   If you haven't already, you should check out what we're building, and why you should work here. We are looking for a self-starting engineer, passionate about applying the latest advancements in LLMs to create user-centric products. In this role, you will stay on top of the latest LLM trends, build evaluation frameworks for AI features, and apply your knowledge with a product-oriented focus. You will be an integral part of the founding team, interacting directly with users to understand their needs and rapidly developing features that deliver significant value. Your work will be pivotal in scaling Granola to its next 100x growth milestone. In this role, you will: * Own the development and application of LLM-based features end-to-end * Build and maintain evaluation frameworks for prompt iteration and semantic retrieval * Optimize embeddings and vector stores for performance and scalability * Interact with users to understand their problems and design solutions * Stay up-to-date with the latest trends in LLMs and applied AI practices * Collaborate with a cross-functional team of engineers, product managers, designers, and other members to create a cutting-edge product Your background looks something like: * Engineering experience at tech and product-driven companies * Shipping multi-provider LLM-based solutions to production (using e.g. OpenAI, Anthropic, Google, etc.) * Proficiency with LLM infra platforms (prompt management, logging/tracing, evals) * Experience designing large-context LLM systems (RAG, knowledge graphs, hybrid search, memory) * Building features end-to-end with TypeScript, React.js, and Node.js As a person, you… * Are first and foremost a builder. * Are excited to work in-person from our office in London (most of the time) * Love working in a startup environment (you either have experience working in a startup or are really drawn to the zero-to-one phase) * Want to be at the cutting edge of building world-class products on top of language models * Are fascinated by LLMs. You love to play with them, figure out what makes them tick and get them to do what you want. You scour the web and reddit for others doing the same (you probably follow @goodside on twitter) * Value working with people who are kind, ambitious and pragmatic About the opportunity We are living in the most exciting time for tool builders since Engelbart's demo in 1968. We want to assemble the best crew to build this future together, here in London. Our compensation philosophy is to pay slightly above market on salary and above market on equity. We do our best work in person, and so our team spends time together five days per week in our new, bright, and spacious office at Old Street. We are happy to offer relocation assistance to candidates who'll be moving to London to join us. Lastly, we think amazing talent comes from all kinds of life journeys and experiences. If what is written above speaks to you, whether you look like a fit on paper or not, please reach out.

ABOUT US: At Fireworks, we’re building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We’ve been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We’re an ambitious, collaborative team of builders, founded by veterans of Meta PyTorch and Google Vertex AI. In the last few months alone we launched Fireworks Training, partnered with Microsoft Azure Foundry, and published research straight from our production systems. A few examples of what that looks like in practice: * Frontier RL is cheaper than the mega-cluster narrative suggests: we ran cross-region rollouts using 98% sparse weight deltas and published what we learned. (blog [https://fireworks.ai/blog/frontier-rl-is-cheaper-than-you-think]) * Open source agents with frontier advisors: matching frontier performance through training and harness engineering. (blog [https://fireworks.ai/blog/open-source-agents-frontier-advisors]) * The fine-tuning bottleneck is not the algorithm: integration friction and iteration speed are what actually stall teams; we documented the patterns across dozens of customer engagements. (blog) [https://fireworks.ai/blog/fine-tuning-bottlenecks] The Role AI Field Engineers at Fireworks are the technical tip of the spear. You embed with our most ambitious customers and technology partners to turn complex AI problems into production systems, fast. The role sits at the intersection of engineering, product, and customer delivery. You are hands-on-keyboard building POCs, MVPs, and production integrations, while also holding your own in executive-level conversations about architecture, strategy, and business outcomes. You spend most of your time building. You ship code, run benchmarks, debug production issues, and architect deployments. But you also lead discovery conversations, align stakeholders, and translate customer pain points into product improvements that compress the feedback loop from field to roadmap. This is a role for engineers who are comfortable on-site with customers, building the relationships and trust that happen in person, not just over a call. The Segment As a Field Engineer in the Enterprise track you will work with large organizations and digital-native companies adopting GenAI across the business. These engagements span more stakeholders and longer cycles, so you will manage executive relationships and align teams while staying hands-on in the code. The emphasis is on pairing strong technical delivery with the executive presence to earn trust across an org: discovery, solution design, POC execution, and the path to production at enterprise scale. What You'll Work On Technical Delivery and Deployment * Build end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases, infrastructure, and constraints. * For customers whose core product is built on GenAI, architect the inference foundations that capability depends on, and size deployments so they can scale in their market without infrastructure becoming the bottleneck. * Run load tests and establish latency, throughput, and cost baselines against realistic customer traffic profiles, and tune deployments to hit those targets * Deploy and validate new model families on inference frameworks (vLLM, SGLang), determining optimal shapes, quantization configs, and serving patterns across workloads. Model Strategy and Fine-Tuning * Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation methodology. * Build and run fine-tuning pipelines directly with customers, navigating trade-offs between model families, compute cost, and quality targets. * Design and implement evaluation frameworks that measure production-quality metrics, not just benchmark scores. Customer Engagement and Stakeholder Management * Many of our customers exist because of GenAI. Help them bake frontier model capabilities into their core offering and turn that into a durable competitive edge. * Lead structured discovery conversations to unpack customer pain points, constraints, and success criteria before proposing solutions. * Own the technical relationship from first engagement through production deployment. Earn trust with ML engineers and VPs in the same meeting. * Spend time on-site with customers. Build trust and momentum in person, embedding with their teams where the work happens. Product Feedback and Platform Improvement * Identify recurring customer pain points and translate them into concrete product proposals, working directly with engineering and product to ship fixes and features. * Codify repeatable deployment patterns and contribute them back to internal tooling, documentation, and the platform itself. * Feed customer signals (deployment patterns, failure modes, feature gaps) back into the product roadmap with specificity and urgency. What We're Looking For Minimum Qualifications * 5+ years in a hands-on, customer-facing technical role: Forward Deployed Engineer, Applied AI Engineer, Solutions Architect, ML Engineer with field exposure, or technical founder. * Demonstrated ability to build production software with customers, not just advise on it. You have shipped code running in someone else's production environment. * Strong Python skills. Comfortable reading, writing, and debugging production code. Familiarity with Kubernetes and infrastructure engineering. * Working knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus). * Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure. * Exceptional communication: able to run a sharp discovery call, present to a VP, and debug a latency issue with an ML engineer in the same afternoon. Preferred Qualifications * 10+ years in technical field or engineering roles. * Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads. * Experience operating as a technical authority inside a customer's environment building within their infrastructure, navigating their constraints, and shipping code that runs in their production systems. * Track record taking GenAI POCs from prototype to production-scale deployments. * Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex). * Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains. WHY FIREWORKS AI? * Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving. * Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally. * Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results. * Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation. Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.