Top Tech Jobs & Startup Jobs

Own infrastructure for deploying and operating customer-specific AI models and services. Responsibilities include AWS cloud and GPU resource management, CI/CD and infrastructure as code, customer data isolation and security, SOC 2 compliance, monitoring and logging, ETL pipelines, model versioning, ML lifecycle management, and automated testing. The role requires strong Python skills, infrastructure or platform engineering experience, startup experience, and technical leadership.

Develop and productionize machine learning models for supply chain forecasting and optimization. Responsibilities include creating model architectures, curating proprietary real-world data, integrating LLM reasoning and tool use, designing evaluations, and continuously improving performance as market conditions change. The role owns the full research cycle from hypothesis through production and requires collaboration in a fast-moving, in-person environment.

Engage with customers to understand operational challenges, frame business problems, conduct technical and value-based demos, and build Python- and SQL-based prototypes. Support evaluations, pilots, implementations, and customer deployments while demonstrating how AI, machine learning, and optimization solve business problems.

Lead and scale a research team from 5 to 20 researchers developing frontier machine learning systems for forecasting, optimization, and control. The role requires strong ML foundations, hands-on experience training and deploying production models, and first- or second-line management experience. Preferred qualifications include LLM infrastructure expertise, quantitative modeling experience, and startup experience. This is an in-person role in San Francisco, five days per week.

Sell Omnifold’s AI-driven forecasting and optimization platform to enterprise customers across supply chain, manufacturing, CPG, retail, and logistics. Responsibilities include self-sourcing pipeline, engaging senior executives, managing complex multi-stakeholder sales cycles, quantifying ROI, closing six- and seven-figure deals, maintaining CRM accuracy, forecasting, and helping build the company’s sales playbook and go-to-market strategy.

Own infrastructure for deploying and operating customer-specific AI models. Responsibilities include production deployments, customer data isolation, security hardening, AWS cloud and GPU resource management, cost optimization, monitoring, logging, ETL pipelines, model versioning, ML lifecycle management, and automated testing. The role requires Python, cloud computing, CI/CD, infrastructure-as-code, security fundamentals, ML workflow familiarity, and a strong computer science background.

Develop and train machine learning models for supply chain forecasting and optimization. Build proprietary data assets, integrate LLM reasoning capabilities, and improve model performance as market conditions change. The role owns the full research cycle from hypothesis through production deployment and requires working with complex, heterogeneous real-world data in a fast-paced, in-person startup environment.

Leads customer-facing applied AI engagements by understanding operational challenges, framing business problems, delivering technical demos, and building Python- and SQL-based prototypes. Supports enterprise sales cycles, evaluations, pilots, implementations, and customer deployments. Requires strong data science and machine learning knowledge, business intuition, project management, and the ability to communicate with technical and non-technical stakeholders.

The Director of Product Marketing will own positioning, messaging, product launches, vertical narratives, storytelling, and campaign architecture for an AI-powered supply chain planning platform. This individual contributor role reports to the CMO, requires strong B2B product marketing expertise, and will initially launch demand planning before building a broader marketing function and team.

Build frontend-focused full-stack applications for AI forecasting products. Create elegant, data-dense interfaces, design human-LLM interactions, make product and architecture decisions, and deliver features end to end from requirements through documentation. The role requires strong computer science fundamentals, TypeScript, React, Tailwind, Python, analytics interface experience, and effective use of modern AI tools.