СШАManager, Field Engineering
1 мес. назад
270k–310k USD / годСШАLeadHybrid
aiinferencefine-tuningtechnical evaluation
Lead and manage a team of field engineers for AI inference and fine-tuning platform adoption with hands-on technical engagement.
О компании
- 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, powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI.
- We're hiring a Manager, Field Engineering to lead a team of Field Engineers driving technical evaluations and production adoption of ' inference and fine-tuning platform. This is a player-coach role — you'll manage and grow a distributed team of hands-on engineers while staying deeply technical yourself, leading engagements end-to-end at some of the most ambitious AI-native companies and enterprises.
- As Manager you will be a critical link between the field and the rest of the company: coaching your field engineers in real time, unblocking complex evaluations, and ensuring every customer engagement meets a high technical bar across latency, throughput, cost, security, and scalability. You'll work closely with regional sales leadership to scale the engagement model, codify the playbook, and build the team.
- This is a hands-on leadership role. Your credibility with field engineers comes from having built production AI systems with customers, not just having managed people who did. You will still get in the weeds when the stakes are highest with shipping POCs, performance optimizations, co—lead training engagements with our research team and debugging alongside your team.
- Our mission is to make AI inference fast, affordable, and production-ready for every developer and enterprise. Our organization is flat, and our team is small and talent dense. We particularly like people who are truth-seeking, passionate, and creative. We enjoy spirited debate, crazy ideas, and shipping code.
- We are building a Field Engineering organization in that same image — engineers who lead with credibility earned in the codebase and the customer's infrastructure, who compress the feedback loop from field to roadmap, and who treat every customer deployment as a chance to make the platform better for everyone.
- 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.
- AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
Обязанности
- Lead, coach, and grow a distributed team of Field Engineers — hiring, onboarding, developing, and holding a high bar for technical excellence and customer outcomes
- Own your team's engagement portfolio: allocate the right engineers to the right pursuits and ensure consistent execution across discovery, demos, POCs, and production integrations
- Lead complex evaluations end-to-end (discovery → architecture → POC → production plan), personally stepping in on the highest-stakes or most technically challenging deals
- Coach AEs and Field Engineers in real time to improve deal quality and close outcomes — reviewing architectures, sitting in on calls, and running sharp post-mortems on wins and losses
- Build and refine the Field Engineering playbook: discovery frameworks, POC templates, reference architectures, and reusable field artifacts
- Serve as the voice of your team and customers internally — systematizing field insights, influencing the product/engineering roadmap, and translating recurring pain points into concrete platform improvements
- Partner with revenue leadership on pipeline health, forecast calls, and territory planning for your region
- Stay hands-on when it matters: build/ship alongside your engineers (POCs/MVPs, load testing, eval + fine-tuning pipelines, model-serving choices across vLLM/SGLang/TensorRT-LLM) and ensure strong post-sales handoffs for onboarding and adoption
- Track and improve your team's operating metrics — win rates and velocity, POC cycle time, utilization, and customer adoption outcomes
Другое
- You have 8+ years of overall experience, including 2+ years managing Field Engineering, Solutions Engineering, Forward Deployed Engineering, or Pre-Sales teams , with hands-on experience in enterprise software or AI infrastructure
- You have a strong technical foundation and fluency in the LLM stack : inference trade-offs, model serving, fine-tuning workflows (SFT; DPO/RFT a plus), and deploying models on GPU infrastructure across AWS, Azure, and GCP
- You have demonstrated ability to build production software with customers , not just advise on it — you have shipped code running in someone else's production environment and can still do it when a deal demands it
- You have a track record of developing engineers — coaching, giving direct feedback, and growing individual contributors into senior and lead roles
- You have proven your ability to partner effectively with Sales while maintaining technical integrity and customer trust in high-stakes deal environments
- You have strong communication skills — able to run a sharp discovery call, present to a VP or CTO, and debug a latency issue with an ML engineer in the same afternoon
- You have a builder mindset : you thrive in fast-moving, startup environments and enjoy creating structure where little exists
- Willingness to travel (up to ~30%) for customer engagements and team onsites
- Technical depth with coaching instinct: able to both architect the solution and teach the engineer next to you why it's right
- Operational rigor: able to run a portfolio of concurrent engagements and hit consistent execution across the team
- Deep familiarity with the AI inference, fine-tuning, and production GenAI market and ecosystem
- Sophisticated understanding of enterprise cloud platforms and complex integration environments, with the ability to translate technical architecture into business value
- Strong written and verbal communication — internal docs, exec-ready summaries, and customer-facing technical artifacts that are sharp and clear
- Commercial instincts: able to size opportunities, understand deal dynamics, and align your team's effort to revenue acceleration
- Comfort with ambiguity: creates process where none exists without slowing the team down