Top Tech Jobs & Startup Jobs

Leads application engineering for an AI-powered email product across backend, mobile, and desktop platforms. Owns architecture for reliable AI workflows, APIs, orchestration, retries, fallbacks, and failure handling. Leads senior engineers through design and code reviews, mentorship, and technical decision-making. Establishes observability, monitoring, testing, and operational excellence while balancing latency, reliability, cost, security, and user experience. Partners with ML leadership to deliver scalable, robust product experiences.

Own AI Investing product areas across financial services, defining user problems, scope, metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex products, launch features, monitor performance, and iterate using data, feedback, research, and production insights. The role requires strong product judgment, execution, prioritization, communication, stakeholder management, and ownership in a fast-moving remote environment.

Own product areas within an AI neobank across financial services such as insurance, payments, savings, investing, and travel. Define user problems, product direction, scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, compliance, operations, and business teams; simplify complex financial journeys; analyze feedback and performance; and continuously iterate products while balancing speed, usability, business impact, compliance, and operational complexity.

Own product areas within BJAK’s AI Neobank across financial services such as insurance, payments, savings, investing, and travel. Define user problems, scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial products; and use research, feedback, metrics, and production insights to improve products after launch.

Own product areas within an AI neobank, defining user problems, roadmaps, success metrics, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify financial products, ship features, monitor performance, and iterate based on data, feedback, research, and production issues. Balance speed, user experience, business impact, compliance, and operational complexity while driving execution with high ownership.

Own product areas within BJAK’s AI Neobank across financial services such as insurance, payments, savings, investing, and travel. Define user problems, scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, compliance, operations, and business teams; simplify complex financial journeys; and use data, feedback, experiments, and production issues to improve products after launch.

Own product areas within an AI neobank across financial services such as insurance, payments, savings, investing, and travel. Define user problems, scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial products; launch features; monitor performance; and iterate using data, feedback, research, and production insights.

Own the architecture and execution of AI-powered email application engineering across backend, mobile, and desktop platforms. Design reliable AI workflows, APIs, orchestration, retries, fallbacks, and failure handling. Lead a small senior engineering team through technical decisions, reviews, mentorship, and hands-on development. Establish observability, monitoring, testing, scalability, and operational excellence while balancing latency, cost, security, reliability, and user experience.

Own the end-to-end machine learning execution layer, including data pipelines, large-model training and fine-tuning, evaluation, inference, deployment, monitoring, and reliability. Architect scalable GPU-based systems, optimize latency and cost, translate research into production products, and establish measurable, safe model-improvement processes. This hands-on technical leadership role requires strong software engineering, systems expertise, independent judgment, and experience shipping production ML systems.

Lead end-to-end machine learning systems from data and model training through evaluation, inference, deployment, and monitoring. Build large-model fine-tuning and evaluation pipelines, optimize GPU-based inference for latency, cost, reliability, and utilization, and develop high-quality training data systems. Partner with research and application engineering to deliver reliable production solutions, resolve issues, and improve models through measurable, safe iteration.