{"id":56773,"url":"https://alion.io/job/fireworks-ai-field-engineer-emea","title":"AI Field Engineer, EMEA","company":{"id":18243,"name":"Fireworks AI","domain":"fireworks.ai","url":"https://alion.io/company/fireworks","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":82,"open_postings":15,"ghost_share":0,"stale_share":0.533,"repost_share":0,"time_to_fill_p50_days":62,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":226000,"max_usd":374000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":18},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"DPO","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GCP","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"SFT","optional":false},{"name":"SGLang","optional":false},{"name":"vLLM","optional":false},{"name":"AI Agents","optional":true},{"name":"Amazon SageMaker","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"Fireworks AI","optional":true},{"name":"Multimodal AI","optional":true},{"name":"PyTorch","optional":true},{"name":"TensorRT","optional":true},{"name":"TensorRT-LLM","optional":true},{"name":"Tool Use","optional":true}],"status":"live","first_seen_at":"2026-07-22T22:31:35Z","employer_posted_date":"2026-07-22","last_verified_at":"2026-10-17T21:53:00Z","board_verified":true,"closed_at":null,"days_open":87,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":87},"description":"About Us:\nFireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI.\nIn 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:\nFrontier 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)\n\nOpen source agents with frontier advisors: matching frontier performance through training and harness engineering. (blog)\n\nThe 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)\n\nThe Role\nAI 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.\nYou 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.\nThe Segment\nAs 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.\nWhat You'll Work On\nTechnical Delivery and Deployment\nBuild end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases, infrastructure, and constraints.\n\nFor 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.\n\nRun load tests and establish latency, throughput, and cost baselines against realistic customer traffic profiles, and tune deployments to hit those targets\n\nDeploy and validate new model families on inference frameworks (vLLM, SGLang), determining optimal shapes, quantization configs, and serving patterns across workloads.\n\nModel Strategy and Fine-Tuning\nGuide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation methodology.\n\nBuild and run fine-tuning pipelines directly with customers, navigating trade-offs between model families, compute cost, and quality targets.\n\nDesign and implement evaluation frameworks that measure production-quality metrics, not just benchmark scores.\n\nCustomer Engagement and Stakeholder Management\nMany 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.\n\nLead structured discovery conversations to unpack customer pain points, constraints, and success criteria before proposing solutions.\n\nOwn the technical relationship from first engagement through production deployment. Earn trust with ML engineers and VPs in the same meeting.\n\nSpend time on-site with customers. Build trust and momentum in person, embedding with their teams where the work happens.\n\nProduct Feedback and Platform Improvement\nIdentify recurring customer pain points and translate them into concrete product proposals, working directly with engineering and product to ship fixes and features.\n\nCodify repeatable deployment patterns and contribute them back to internal tooling, documentation, and the platform itself.\n\nFeed customer signals (deployment patterns, failure modes, feature gaps) back into the product roadmap with specificity and urgency.\n\nWhat We're Looking For\nMinimum Qualifications\n5+ 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.\n\nDemonstrated ability to build production software with customers, not just advise on it. You have shipped code running in someone else's production environment.\n\nStrong Python skills. Comfortable reading, writing, and debugging production code. Familiarity with Kubernetes and infrastructure engineering.\n\nWorking knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus).\n\nExperience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure.\n\nExceptional 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.\n\nPreferred Qualifications\n10+ years in technical field or engineering roles.\n\nExperience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads.\n\nExperience 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.\n\nTrack record taking GenAI POCs from prototype to production-scale deployments.\n\nExperience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex).\n\nExperience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains.\n\nWhy Fireworks?\nSolve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.\n\nBuild What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally.\n\nOwnership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI-no bureaucracy, just results.\n\nLearn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation.\n\nFireworks AI is an equal-opportunity employer. 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