I

Inferact

Open-source LLM inference engine with scalability

Go-to-Market Engineer

Full-Time
No salary listed
Mid
San Francisco, CA, USA
In Person
Sales & Account ManagementSales Operations
H1B Sponsorship Available

About the job

Requirements
  • Experience building or operating go-to-market systems, revenue operations, sales operations, or GTM engineering workflows in a startup or high-growth technology environment.
  • Hands-on experience implementing and structuring a CRM, including account and opportunity data models, pipeline stages, required fields, routing, process hygiene, and reporting.
  • Ability to work with data, APIs, integrations, enrichment sources, automation tools, and lightweight scripting to turn fragmented commercial information into usable workflows.
  • Understanding of early-stage sales operations, including ideal customer profile definition, prospect research, pipeline management, handoffs, follow-up, and distinguishing useful process from unnecessary process.
  • Ability to identify the highest-leverage operating system to build for a blank-sheet problem and deliver it without detailed requirements.
  • Ability to work directly with commercial leadership, founders, product, engineering, and external implementation partners.
  • Ability to translate commercial needs into clear system and data requirements and communicate tradeoffs to technical and non-technical stakeholders.
Responsibilities
  • Build Inferact's commercial operating system from the ground up, turning fragmented information into systems that make the team faster, more focused, and more effective.
  • Create a clean commercial source of truth, help define the sales process, mine ecosystem and community data to identify high-value prospects and warm paths, and build workflows and automation for targeted selling.
  • Evolve the system toward scalable prospecting, reporting, and commercial execution as the ideal customer profile becomes clearer, while avoiding unnecessary process.
  • As the sales team grows, help define representative onboarding and training and support sales compensation planning; these broader revenue-operations needs are expected no earlier than 2027.
Desired Qualifications
  • Experience building go-to-market infrastructure from zero to one before a company had a mature CRM, documented ideal customer profile, or standardized sales process.
  • Experience mining proprietary or community data sources to identify high-value accounts, warm paths, customer signals, or technically relevant prospects.
  • Experience with AI-native or modern go-to-market workflows, including research automation, enrichment, account intelligence, workflow orchestration, and personalized outbound support.
  • Experience supporting developer tools, AI infrastructure, open-source software, cloud platforms, or another highly technical B2B product.
  • Comfort combining hands-on go-to-market engineering with revenue-operations judgment; experience defining representative onboarding and training or supporting sales compensation plans.
  • Experience building a CRM and commercial operating cadence from scratch for an early-stage startup.
  • Experience creating account research, enrichment, warm-introduction mapping, or prospect prioritization systems that improved seller focus.
  • Experience automating repetitive go-to-market workflows while preserving human judgment for strategic accounts.
  • Experience working with open-source communities or product-usage data as a source of commercial intelligence.
  • Experience building internal go-to-market tools, dashboards, agents, or data pipelines that became core infrastructure for a commercial team.

About the company

I

Inferact

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Inferact builds AI inference infrastructure by maintaining vLLM, an open-source LLM inference engine, and offering a managed enterprise inference service. vLLM uses PagedAttention to optimize GPU memory, cutting inference costs and latency while preserving model quality. The company supports multiple architectures and hardware, aligns with PyTorch Foundation governance, and pursues open-source collaboration alongside a commercial platform. Its goal is to turn AI inference into a reliable, scalable operating layer of the AI stack, separating model deployment from application development.

Company Size

11-50

Company Stage

Seed

Total Funding

$150M

Headquarters

San Francisco, California

Founded

2025

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Simplify Jobs

Simplify's Take

What believers are saying

  • Inferact raised $150 million in January 2026 at an $800 million valuation.
  • TPU megakernel work reached 709 tokens per second on Kimi K3.
  • Open-source leadership keeps contributors flowing from Meta, Google, and Amazon ecosystems.

What critics are saying

  • NVIDIA Dynamo commoditizes vLLM, SGLang, and TensorRT-LLM beneath NVIDIA-controlled routing.
  • RadixArk and ZML target the same inference layer, compressing pricing power.
  • If managed inference lags open-source innovation, customers keep vLLM and skip Inferact.

What makes Inferact unique

  • Inferact owns vLLM, the default inference engine for 500-plus architectures.
  • Google partnered with Inferact on TPU vLLM support on September 14, 2026.
  • Inferact ships day-zero quantized releases like Qwen3.8-2.4T-A95B with NVIDIA and AMD.

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Benefits

  • Health Insurance
  • Dental Insurance
  • Vision Insurance
  • 401(k) Company Match

Growth & Insights and Company News

Headcount

6 month growth

6%

1 year growth

10%

2 year growth

10%
vLLM
Aug 12th, 2026
Day 0 support for Qwen3.8-2.4T-A95B on vLLM.

Day 0 support for Qwen3.8-2.4T-A95B on vLLM. vLLM Team and Inferact vLLM is announcing Day-0 vLLM support for Qwen3.8-2.4T-A95B. This is the first model from the Qwen family to bring a Qwen-Max-class model to open-weight release. Qwen3.8-2.4T-A95B is built on the Qwen 3.5 architecture and runs on vLLM out of the box. In addition to the official FP8 and BF16 checkpoints, Inferact has released MXFP4 and NVFP4-quantized weights that match full-precision quality while significantly reducing memory and bandwidth overhead. Qwen3.8-2.4T-A95B is a 2.4-trillion-parameter sparse MoE model featuring 512 experts. Within its 92-layer hybrid backbone, full attention is applied at every 4th layer while the remaining 69 layers run linear attention. As one of the largest open-weight models released to date, running inference requires at least two NVIDIA B300 / AMD MI355X nodes (or a single node for the FP4 quantized version). Tl;dr. * Day-0 support: Qwen3.8-2.4T-A95B reuses the Qwen 3.5 architecture and runs on vLLM from day one with no architecture changes required. * Flexible precision: FP8, BF16, NVFP4, and MXFP4 checkpoints are available. * Multi-vendor optimization: Validated across hardware partners including NVIDIA and AMD. Quick start. For NVFP4: For MXFP4: See the vLLM recipes for the full serving guide and recommended flags. FP4 quantization: quality at lower cost. To minimize inference costs and maximize GPU memory efficiency, the Inferact team quantized selected layers - including the routed experts - to FP4 weights using Round-to-Nearest (RTN) quantization with activation calibration to enable 4-bit activations. vLLM ran initial verifications to confirm that quantization accuracy remains intact. Note that increasing the reasoning budget is required to reproduce these evaluation results. | Benchmark | FP8 | NVFP4 | | GSM8K (strict / flexible) | 89.61% / 90.52% | 90.37% / 91.05% | | AIME25 @3 (avg / pass) | 87.78% / 93.33% | 92.22% / 96.67% | Optimizations. To enable efficient inference for this 2.4T parameter model, vLLM collaborated closely with NVIDIA and AMD to develop optimized kernels based on existing Qwen 3.5 support. On NVIDIA platforms, NVIDIA and Inferact co-developed ultra-fast kernels for Linear Attention (Gated Delta Rule), Attention (GQA), Dense GEMMs, and MoE routing. New fused kernels were added to reduce communication overhead. Significant effort was also dedicated to identifying the best decomposition of work to maximize performance, including combining Data Parallelism and Tensor Parallelism for Attention and Expert Parallelism for the MoE. On AMD Instinct GPUs, vLLM accelerates Qwen3.8 with AITER-fused Gated DeltaNet decode, attention, and MoE kernels, reducing kernel-launch and data-movement overhead. For Shared Expert MoE, the shared-expert path leverages highly optimized hipBLASLt GEMM kernels, while routed experts use AITER FusedMoE. AMD Quark quantization support enables efficient MXFP4 deployment, substantially reducing model memory requirements while maintaining strong accuracy. Deployment tips. The Qwen 3.8 model card recommends the following generation parameters for optimal performance: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0 Here is a Python client snippet to query the model once the vLLM server is running. Because Qwen 3.8 is a reasoning model, ensure you allocate a sufficient token budget for agentic workflows by setting a high max_tokens value. Acknowledgements. vLLM thank the Qwen team for releasing the model weights as well as their ongoing collaboration, and its hardware partners, NVIDIA and AMD, for their joint engineering contributions. vLLM also thank the Inferact team for delivering quantized checkpoints and end-to-end vLLM integration, as well as the broader vLLM community. Thanks for its inference partners, including DigitalOcean, Together AI, who helped with early testing.

TechCrunch
Jan 22nd, 2026
Inference startup Inferact lands $150M to commercialize vLLM | TechCrunch

The seed round values the newly formed startup at $800 million.