2027 Summer Intern – Machine Learning Engineer, AV/AI Platform
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Lead partnership strategy to bundle Fireworks with OEM and silicon partners into integrated AI factory offerings. You'll define reference architectures, structure commercial agreements, and execute joint go-to-market plans from initial conversations through revenue. This role requires 10+ years in enterprise infrastructure with proven experience building and scaling at least one joint offering, plus key relationships at Dell, HPE, NVIDIA, AMD, or equivalent companies. Understanding integrated offerings and AI factory stack fundamentals is essential.
Written from this posting by Neural Jobs AI. The full description is below.
Fireworks 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.
About the role
Enterprises are standing up AI factories: full-stack, on-prem and colocated deployments of compute, networking, storage, and models. Today they assemble that stack themselves and figure out the software layer on their own.
We're hiring a Partnerships Lead to collapse it into a single, pre-integrated bundle. You'll assemble alliances across OEMs (Dell, HPE, and others), silicon (NVIDIA and others), packaged with Fireworks as the operating system on top.
You'll own the bundle definition, joint pricing, go-to-market plan end to end, from first alliance conversation to repeatable revenue. The playbook doesn't exist yet. You'll write it: which OEM to lead with, how to structure the commercial stack, and what the joint reference architecture looks like. If you want to define the AI factory category rather than inherit one, this is the role.
What you'll own
Bundle construction
Build alliances across the stack
Define the reference bundle with Fireworks as the serving and orchestration layer
Validate joint architectures with Fireworks engineering
Structure and negotiate the commercial agreements
Go-to-market and execution
Build and execute the joint GTM plan: ICP, field plays, targets
Enable field sellers on joint offering
Drive deals end to end with partner sellers and own the forecast
You're a good fit if you have
10+ years in enterprise infrastructure, with at least one joint offering you built and scaled
Key relationships inside at least two of: Dell, HPE, NVIDIA, AMD, or equivalent OEM
Understand the AI factory stack
Experience in integrated offerings (appliance or validated design programs)
Nice to have
Background selling into on-prem or sovereign AI deployments, where the customer runs the hardware and the vendor runs the software
Familiarity with LLM inference economics: throughput per GPU, utilization, and why the software layer determines the ROI of the whole bundle
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.
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Fireworks AI is a generative-AI inference platform focused on fast, low-cost serving and fine-tuning of open models for production workloads.
Founded in 2022 by former PyTorch engineers, it optimises model serving for latency, throughput and cost.
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