Top Tech Jobs & Startup Jobs

Own the technical sales process for North American opportunities, partnering with the founding Account Executive. Lead technical discovery, demos, benchmarks, POCs, architecture reviews, migration planning, security responses, and production onboarding. Build scalable sales engineering assets, including demo environments, benchmark tools, reference architectures, and enablement materials. Translate customer feedback into product and engineering priorities while supporting technical forecasting and representing the company at developer events.

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.

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.

The AI Researcher will develop and evaluate model distillation techniques, analyze trade-offs, run experiments, publish research, and collaborate for production deployment.

The AI Researcher will design and execute research on multilingual datasets, improve cross-lingual transfer in language models, and publish findings at top conferences while translating research into production improvements.