Job Overview
Location
San Francisco
Job Type
Full-time
Category
Software Engineering
Date Posted
June 21, 2026
Full Job Description
đź“‹ Description
- • Build and operate production AI systems deployed directly within customer environments, taking full ownership of system reliability, performance, and business impact
- • Design and implement compound AI workflows that integrate LLMs, prompts, agents, tools, retrieval systems, evaluators, and feedback loops into cohesive, production-grade applications
- • Develop clean, maintainable Python services that integrate AI capabilities into customer data platforms, APIs, and existing enterprise applications
- • Operate live AI systems by monitoring behavior, identifying failure modes, debugging issues, and iterating rapidly to improve quality and user value
- • Construct evaluation frameworks, test cases, feedback mechanisms, and observability patterns to measure and enhance AI system performance over time
- • Collaborate directly with customer stakeholders and subject matter experts to understand operational workflows, clarify ambiguous requirements, and adapt systems to evolving needs
- • Use AI-native engineering tools daily to accelerate code development, debugging, data analysis, and system experimentation
- • Work alongside AI Engineers, AI Strategists, and other team members to make pragmatic design decisions balancing speed, robustness, maintainability, and customer impact
- • Take accountability for the production outcomes of all components, workflows, and systems you build, ensuring they deliver intended business value under real-world constraints
- • Translate messy, real-world operational needs into concrete system behavior and technical specifications that align with enterprise constraints and user expectations
- • Continuously improve deployed systems through iterative feedback, data-driven insights, and production-level iteration — not demos or prototypes
- • Travel to customer sites 10–30% of the time to support deployments, gather requirements, and ensure successful integration in live environments
- • Work in a hybrid model requiring 3+ days per week (Tuesday–Thursday) in the San Francisco office
- • Engage with cutting-edge AI technologies and real-world business problems across top enterprises in telecom, healthcare, insurance, manufacturing, and consumer goods
- • Participate in a mission-driven culture focused on curiosity, pragmatism, and excellence in deploying AI systems that affect millions of user interactions and critical workflows
🎯 Requirements
- • 2+ years of software engineering experience
- • Ownership mentality for AI systems, including responsibility for production outcomes and technical decision-making
- • Experience building AI systems using LLMs or other models, composing prompts, agents, tools, retrieval, evaluators, and workflows into end-to-end applications
- • Strong engineering fundamentals in Python, including production software development, debugging, testing, versioning, and code review
- • AI-native working style: daily use of AI tools for coding, debugging, data analysis, and automation
- • Comfort working directly with customer teams, communicating system limitations and tradeoffs, and adapting quickly to new domains
🏖️ Benefits
- • Base salary range of $150K – $250K, plus meaningful equity
- • 100% covered medical, dental, and vision for employees and dependents
- • 401(k) with additional perks including commuter benefits and in-office lunch
- • Access to state-of-the-art AI models and generous usage of modern AI tools
- • Ownership of high-impact projects across top enterprises
- • Hybrid work model with 3+ days per week in the San Francisco office
Skills & Technologies
See exactly how your profile matches this role — strengths, skill gaps, and what to do about them.
About Distyl Inc.
Distyl is a cloud-native platform designed to simplify and accelerate the development and deployment of machine learning (ML) models. It provides a unified environment for data preparation, model training, versioning, and deployment, enabling data scientists and ML engineers to move from experimentation to production faster. The platform offers features such as automated data pipelines, managed training infrastructure, and scalable model serving. Distyl aims to reduce the complexity and operational overhead associated with MLOps, allowing organizations to focus on building and deploying impactful ML solutions. It supports various ML frameworks and integrates with existing cloud infrastructure.
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