Top Tech Jobs & Startup Jobs

Founding Sales Engineer partnering with the founding Account Executive to own technical sales wins across North America. Responsibilities include technical discovery, live demos, POCs, benchmarks, architecture reviews, migration planning, security responses, onboarding, and production support. The role also builds scalable sales engineering assets, feeds product and engineering insights, and represents the company at technical events. Candidates need hands-on Python skills, modern LLM inference expertise, cloud infrastructure knowledge, and strong communication.

Own full-cycle sales for Featherless’s AI inference cloud, from prospecting and discovery through technical demos, proposals, negotiation, and close. Build relationships with CTOs, ML engineers, and infrastructure leaders; close mid-market and enterprise deals; expand existing accounts; maintain HubSpot CRM accuracy and forecasting; collaborate with CRO, BDR, DevRel, product, and engineering; and help establish repeatable sales and go-to-market processes.

Build and qualify enterprise and AI startup pipeline, manage inbound requests and outreach automation, re-engage users, track CRM and pipeline metrics, and attend events to generate relationships. The role requires technical fluency in AI, strong communication, entrepreneurial initiative, and comfort engaging developers, founders, researchers, and enterprises. Candidates should have 1–2 years of experience in sales, developer relations, or entrepreneurship.

Provide front-line technical support through live chat, Discord, and email for AI platform users. Diagnose and escalate platform issues, publish announcements, maintain community engagement, identify customer feedback and growth opportunities, and coordinate with engineering, product, growth, and BDR teams. Improve support efficiency through documentation, canned responses, help-center content, and AI-assisted workflows.

Create and maintain product documentation, blog posts, website copy, and walkthrough videos. Build and manage a content calendar, identify growth opportunities, use internal tools and data to optimize content, and collaborate with product and engineering to translate technical concepts into clear, growth-focused content.

Design and maintain multilingual datasets, develop data pipelines, implement quality filters, and analyze dataset biases while collaborating with researchers.

As an AI Researcher, you will optimize inference performance for large-scale machine learning models by improving latency, throughput, and cost efficiency through various model and systems-level optimizations.

The AI Researcher will develop training optimization techniques, improve large model training efficiency, run experiments and publish research findings.

Optimize inference latency and throughput for large-scale ML models, collaborating on performance tuning, and building inference-serving systems.

The ML Engineer will optimize large-scale model training pipelines, improve distributed training strategies, build robust infrastructure, and collaborate on training techniques and performance metrics.