Forward Deployed Applied Scientist II (Agentic AI)

DigitalOcean•Denver, CO

1d•Remote

About The Position

DigitalOcean’s Forward Deployed Engineering team works at the intersection of AI research, production deployment, and customer impact. The Forward Deployed Applied Scientist embeds with startups, technology innovators, and AI-native enterprise partners to design, build, and deploy custom autonomous-agent architectures on DigitalOcean’s AI infrastructure. The role turns customer requirements into reliable, production-ready agentic systems and helps improve DigitalOcean’s AI platform through direct field feedback. The scientist designs multi-agent workflows, autonomous reasoning loops, and tool-augmented LLM architectures; prototypes agent frameworks and hardens them for enterprise-scale production, including stateful memory. The role also develops reusable evaluation frameworks and safety guardrails to assess precision, security, latency, and cost. As an early user of DigitalOcean’s AI platform capabilities, the scientist surfaces operational insights and scaling bottlenecks to Product Engineering and Research teams. Travel of up to 30% is required for customer engagements, workshops, conferences, and internal collaboration.

Requirements

At least 6 years of hands-on experience in Applied AI, Machine Learning, or Data Science.
Proven experience building multi-agent orchestration engines and working with tool-use or function-calling pipelines, MCP, structured outputs, dynamic planning, and persistent memory models.
Strong production Python and systems-engineering skills, including Pydantic, FastAPI, Asyncio, and PyTorch.
Customer-facing engineering mindset, empathy, and clear technical communication skills, including the ability to explain complex AI trade-offs to engineering leads and executive sponsors.
Bias for action, ownership, and comfort working in a fast-moving environment.
Ability to travel up to 30%.

Nice To Haves

Ph.D. or master’s degree in Computer Science, Machine Learning, AI, or a related technical field.
Experience with deep-learning frameworks such as PyTorch or TensorFlow, distributed training tools, and agentic AI frameworks.
Understanding of transformer types and state space models.
Research experience, publications, or patents, and knowledge of current research in LLMs, VLMs, and agentic frameworks.
Experience translating complex business tasks into AI engineering solutions and collaborating with client teams such as CTOs and AI leads.
Experience tuning model workloads for different accelerator architectures.
Experience collaborating with customers, model vendors, or ecosystem partners on benchmarking, optimization, fine-tuning, or launch readiness.

Responsibilities

Design and implement production-ready multi-agent workflows, autonomous reasoning loops, and tool-augmented LLM architectures for customer challenges.
Work directly with customers, CTOs, and AI engineering leads to translate ambiguous business requirements into high-performance, deterministic AI workflows.
Prototype agentic frameworks such as LangGraph, AutoGen, or custom execution graphs, and prepare them for enterprise-scale production and stateful memory retention.
Develop reusable evaluation frameworks and safety guardrails to monitor reasoning precision, execution security, latency, and cost-efficiency.
Share customer deployment feedback with AI/ML Product teams and turn deployment friction into platform enhancements.
Validate DigitalOcean AI platform capabilities, including its inference engine, runtimes, orchestration systems, GPU platforms, and deployment workflows.
Report operational insights, architectural gaps, and scaling bottlenecks to Product Engineering and Research teams.
Travel up to 30% for customer engagements, strategic workshops, conferences, and internal collaboration.