Jobs at Deep Infra Inc.

Early-career Software Engineer building and scaling AI model inference infrastructure. Responsibilities include developing and testing inference solutions, optimizing models with Python and related technologies, monitoring production serving systems, fixing bugs, participating in code reviews, and exploring AI/ML techniques to improve performance. The role targets candidates with 0–2 years of experience, including internships.

Identify and prospect high-value AI and enterprise accounts through email, phone, LinkedIn, and events. Research prospects, personalize messaging, qualify opportunities, run initial conversations, and book meetings for Account Executives. Partner on account mapping and multi-threading, use AI tools to improve outreach, and stay informed on AI inference, open-source models, and GPU trends.

Designs, deploys, and operates high-performance data center infrastructure across multiple colocation sites. Responsibilities include network architecture, site bring-ups, rack and power planning, cabling, hardware installation, Linux operations, automation, monitoring, infrastructure project ownership, vendor coordination, capacity planning, and troubleshooting. The role also supports GPU infrastructure, private cloud, Kubernetes, internet routing, procurement, and commercial negotiations while requiring travel to data centers for hands-on deployment and support.

Own technical customer relationships for an AI inference platform. Diagnose and resolve production issues involving latency, errors, rate limits, billing, quotas, model behavior, and API integrations using logs, metrics, dashboards, and terminal tools. Advise customers on platform usage, capacity planning, GPU sizing, deployments, and cost optimization. Partner with Engineering and Product on bugs, feature requests, documentation, and permanent fixes while independently managing customer issues end to end.

Owns DeepInfra’s usage-to-revenue billing pipeline, including metering, invoicing, payment performance, reconciliations, fraud prevention, dunning, collections, and pricing-model launches. Partners with Engineering, Finance, Sales, and the Controller to manage billing systems, revenue accounting workflows, payment gateways, and scalable financial operations for self-serve and enterprise customers.

The Director of FP&A will build and lead financial planning, forecasting, budgeting, and reporting. Responsibilities include owning the consolidated three-statement model, analyzing AI inference unit economics, preparing board and investor materials, developing dashboards, automating finance systems, and evaluating infrastructure, CapEx, debt, and equity decisions. The role partners with founders and cross-functional leaders to guide growth and investment strategy.

Own global accounting close, financial reporting, usage-based revenue reconciliation, infrastructure accounting, audits, tax compliance, treasury, working capital, and internal controls. Lead ERP implementation and financial systems integrations while establishing scalable accounting policies and governance for a rapidly growing AI infrastructure company. Partner with engineering, finance, and company leadership across global entities, jurisdictions, and currencies.

Design, develop, optimize, and operate production inference systems for open-source AI models. Build and test model-serving solutions using Python, C++, CUDA, and NCCL; monitor and debug production systems; improve performance; apply AI/ML techniques; and contribute to system design, code reviews, and end-to-end delivery.

Build and lead DeepInfra’s product marketing strategy for AI inference infrastructure. Own positioning, ICP definition, messaging, website, product launches, technical narratives, enterprise sales enablement, ABM campaigns, lead scoring, and marketing-to-sales processes. Partner closely with founders, engineering, product, and sales to drive developer conversion and enterprise growth. The role requires hands-on execution across technical content, advertising, sales collateral, analytics, and market positioning.

Lead growth and demand generation for a B2B AI infrastructure startup. Build PLG funnel, run paid and organic developer campaigns, own SEO/content, implement lead scoring and ABM, create enterprise sales enablement materials, and set up growth analytics and lifecycle marketing to drive awareness, activation, and customer acquisition.

Work with engineers to design, implement, and deploy scalable inference solutions for top AI models. Implement and optimize models (Python, C++, CUDA, NCCL), monitor live services, develop features, fix bugs, participate in code reviews, and collaborate in agile teams.

Customer-facing engineer who owns technical wins from POC to production: designs and runs reproducible inference benchmarks, tunes model-to-hardware deployments, conducts vendor bake-offs, handles security/compliance, drives post-signature account health, and produces reusable benchmark reports and reference architectures to accelerate sales and deployments.

Own full-cycle enterprise deals from prospecting to contract execution. Build and manage pipeline, run discovery, lead POCs and technical validation with engineering, guide onboarding, negotiate pricing, and close contracts while partnering with GTM and Engineering teams.

Assist the engineering team in designing, developing, and deploying scalable AI models. Responsibilities include implementing models, optimizing code, and participating in collaboration activities.

As a Software Engineer, you will collaborate on designing, developing, and testing AI models, maintain production systems, and explore new techniques within a small, impactful team.

Design and implement AI model inference solutions, optimize performance, own production systems, and collaborate in engineering best practices.

Spearhead developer community building and content efforts, creating engaging technical resources and driving the adoption of DeepInfra's AI services while collaborating across departments and representing the company at events.

Design and develop inference solutions for AI models, optimize performance, and maintain production systems while owning end-to-end problems.