6+ years of hands-on experience in Applied AI, Machine Learning, or Data Science.
Proven expertise in building multi-agent orchestration engines and dynamic planning.
Strong skills in production-ready Python (Pydantic, FastAPI, Asyncio, PyTorch).
High empathy and crisp technical communication skills for customer-facing roles.
A bold, action-oriented mindset with a strong sense of ownership over customer experience.
Responsibilities
Design and implement sophisticated multi-agent workflows and autonomous reasoning loops.
Embed with tech innovators to transform ambiguous business requirements into AI workflows.
Prototype cutting-edge agentic frameworks and prepare them for enterprise-scale production.
Develop reusable evaluation frameworks and safety guardrails for agentic systems.
Act as a strategic feedback link between customers and AI/ML Product teams to enhance platform capabilities.
Serve as the 'first customer' for DigitalOcean's AI-native platform, providing operational insights and identifying gaps.
Travel up to 30% for customer engagements and strategic workshops.
Benefits
Remote work flexibility.
Opportunity to work directly with high-growth startups and tech innovators.
Engagement in cutting-edge AI research and deployment.
Access to a high-performance AI infrastructure.
Collaboration with core AI/ML Product teams for platform enhancements.
Full Job Description
The Forward Deployed Engineering (FDE) team operates at the intersection of AI research, production deployment, and customer impact. As an Agentic AI Applied Scientist, you won't sit in an isolated research lab-you will embed directly with high-growth startups, tech innovators, and AI native enterprise partners. You will design, build, and deploy custom autonomous agent architectures running on DigitalOcean's high-performance AI infrastructure. What You'll Do
Architect Production-Ready Agentic Frameworks: Design and implement sophisticated multi-agent workflows, autonomous reasoning loops, and tool-augmented LLM architectures capable of resolving complex, real-world customer challenges.
Direct Customer Integration: Embed deeply with tech innovators, CTOs, and AI engineering leads to transform ambiguous business requirements into high-performance, deterministic AI/Agentic workflows.
Synthesize Research and Deployment: Quickly prototype cutting-edge agentic frameworks-leveraging LangGraph, AutoGen, or custom execution graphs-and harden them for enterprise-scale production and stateful memory retention.
Drive Reliability and Evaluation: Develop comprehensive reusable Evals frameworks and safety guardrails to monitor reasoning precision, execution security, latency, and cost-efficiency across agentic systems.
Optimize the DigitalOcean Ecosystem: Serve as a strategic feedback link between our customers and core AI/ML Product teams, converting field deployment friction into foundational platform enhancements.
Platform Validation & Product Acceleration: Act as the "first customer" for DigitalOcean's AI-native platform capabilities including Inference Engine, runtimes, orchestration systems, GPU platforms, and deployment workflows. Surface real-world operational insights, architectural gaps, and scaling bottlenecks directly to Product Engineering and Research teams.
Travel & Collaboration Requirements: Ability to travel up to 30% for customer engagements, strategic workshops, conferences, and internal collaboration.
What You'll Add to DigitalOcean
6+ years of hands-on experience in Applied AI, Machine Learning, or Data Science.
Expertise in Multi-agent Frameworks: Proven experience building multi-agent orchestration engines, tool-use / function-calling pipelines, MCP, structured outputs, dynamic planning, and persistent memory models.
Production Python & Systems Engineering: Strong skills in writing clean, production-ready Python (Pydantic, FastAPI, Asyncio, PyTorch).
Customer-Facing Engineering Mindset: High empathy, crisp technical communication, and the ability to articulate complex AI trade-offs to both engineering leads and executive sponsors.
The DO "Shark" Mentality: You think big, bold, and scrappy. You have a bias for action and a powerful sense of ownership over the customer experience.
Preferred Qualifications
Education: Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related technical field.
Applied Science Experience: Experience in deep learning frameworks (like PyTorch or TensorFlow), distributed training tools as well as various agentic AI frameworks. Solid understanding of various types of transformers and state space models.
Research Experience: Experience in publications, patents and Knowledge of latest research in the field of LLM, VLM, Agentic frameworks.
Customer Empathy & Technical Leadership: Ability to translate complex business tasks into AI engineering solutions and collaborate directly with client teams (CTOs, AI Leads).
Agility: Comfortable navigating fast-moving environments and tuning model workloads for diverse accelerator architectures.
Vendor & Strategic Partnership Collaboration: Experience collaborating with customers, model vendors, or ecosystem partners on benchmarking, optimization, finetuning, or launch readiness initiatives.
