Senior Forward Deployed Applied Scientist I (Agentic AI)
DigitalOcean · Posted Sep 30
Cloud infrastructure, virtual machines, managed database, and AI inference services
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About the role
DigitalOcean's Forward Deployed AI (FDE) team operates at the intersection of AI research, production deployment, and customer impact, embedding directly with high-growth startups, tech innovators, and AI native enterprise partners. The team designs and deploys custom autonomous agent architectures running on DigitalOcean's high-performance AI infrastructure. This role builds multi-agent workflows, tool-augmented LLM architectures, and reusable evals and safety guardrails, while feeding field deployment learnings back to core AI/ML Product teams.
What you will 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.
Skills used in this role
What the employer is looking for
- 4+ 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.
- Travel & Collaboration Requirements: Ability to travel up to 30% for customer engagements, strategic workshops, conferences, and internal collaboration. Ability to consistently overlap with North American business hours, including availability until at least noon Eastern Time, to collaborate effectively with customers, Product, Engineering, and go-to-market teams.
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.
Benefits and support
- Reimbursement for relevant conferences, training, and education
- Access to LinkedIn Learning's 10,000+ courses
- Employee Assistance Program
- Local Employee Meetups
- Flexible time off policy
- Bonus in addition to base salary based on company and individual performance
- Equity compensation for eligible employees, including equity grants upon hire
- Option to participate in our Employee Stock Purchase Program
About DigitalOcean
DigitalOcean is an American cloud infrastructure provider and AI-native cloud platform built for developers, startups, and small-to-medium-sized businesses. The company provides simple, scalable virtual machines, managed databases, Kubernetes, and specialized inference engines designed to run modern application and agentic AI workloads.
- Industry
- Cloud Computing
- Company size
- 1001-5000 employees
- Founded
- 2011-06-24
- Location
- Broomfield, Colorado, USA
- Funding stage
- Public Company
Funding
Public Company · $174M raised
- 2013-07-01Seed$3.2M
- 2014-03-01Series A$37.2M
- 2014-12-01Debt Financing$50M
- 2015-07-01Series B$83M
- 2016-04-01Debt Financing$130M
- 2020-05-01Venture Round$50M
- 2021-03-24IPO$775.5M
Leadership
Chief Executive Officer
Chief Financial Officer
Chief Product and Technology Officer
Chief Marketing Officer
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