Machine Learning Engineer, Support Experience
Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities.
What this role actually needs.
Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Responsibilities: - Design and implement state-of-the-art ML models and large scale ML systems for enhancing self-serve support capabilities, balancing ML principles, domain knowledge, and engineering constraints - Develop and optimize contextual conversation models and ML-powered resolution flows for common support scenarios, using tools such as PyTorch, TensorFlow, and XGBoost - Create and refine pipelines for training and evaluating models in both offline and online environments, with a focus on improving support quality and user satisfaction - Implement ML features that streamline information collection and processing for support agents, enhancing overall support efficiency - Collaborate with product, strategy, and content teams to propose, prioritize, and implement new AI-driven support features and improve answer capabilities - Stay current with the latest developments in ML/AI, particularly in natural language processing and conversational AI, and apply innovative ideas to improve support experiences Requirements: - Bachelor's Degree in ML/AI or related field (e.g. math, physics, statistics) - 3+ years in AI/ML and backend engineering, including building and operating production ML systems at global scale with stringent SLOs—balancing reliability, latency, and cost—with privacy, security, and compliance by design. - Deep and up-to-date applied LLM experience: RAG/embeddings, tool use/function calling, agentic planning/orchestration architectures, post-training methods, code generation, benchmarks and evaluations, etc. Familiarity with classical ML methods and common frameworks e.g. Pytorch, TensorFlow. - Proficient in Python; strong distributed systems and data science fundamentals. - Experience working closely with product management, design, other engineers, and other cross-functional partners. - Strong technical leadership and communication: mentoring and elevating engineers, elevating AI/ML awareness and posture within organizations, setting architectural direction, and driving alignment in ambiguity. Company context: Stripe builds financial infrastructure for internet businesses, with strong hiring across platform, product, data, and AI-adjacent teams.
Day-to-day expectations
Stripe lists these responsibilities for the Machine Learning Engineer, Support Experience role.
- Design and implement state-of-the-art ML models and large scale ML systems for enhancing self-serve support capabilities, balancing ML principles, domain knowledge, and engineering constraints
- Develop and optimize contextual conversation models and ML-powered resolution flows for common support scenarios, using tools such as PyTorch, TensorFlow, and XGBoost
- Create and refine pipelines for training and evaluating models in both offline and online environments, with a focus on improving support quality and user satisfaction
- Implement ML features that streamline information collection and processing for support agents, enhancing overall support efficiency
- Collaborate with product, strategy, and content teams to propose, prioritize, and implement new AI-driven support features and improve answer capabilities
- Stay current with the latest developments in ML/AI, particularly in natural language processing and conversational AI, and apply innovative ideas to improve support experiences
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- Bachelor's Degree in ML/AI or related field (e.g. math, physics, statistics)
- 3+ years in AI/ML and backend engineering, including building and operating production ML systems at global scale with stringent SLOs—balancing reliability, latency, and cost—with privacy, security, and compliance by design.
- Deep and up-to-date applied LLM experience: RAG/embeddings, tool use/function calling, agentic planning/orchestration architectures, post-training methods, code generation, benchmarks and evaluations, etc. Familiarity with classical ML methods and common frameworks e.g. Pytorch, TensorFlow.
- Proficient in Python; strong distributed systems and data science fundamentals.
- Experience working closely with product management, design, other engineers, and other cross-functional partners.
- Strong technical leadership and communication: mentoring and elevating engineers, elevating AI/ML awareness and posture within organizations, setting architectural direction, and driving alignment in ambiguity.
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Source: stripe.com · Source ID: 7813942 · Confidence: 94/100 · Last checked: May 7, 2026
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