Forward Deployed Applied Scientist II (Agentic AI)

DigitalOcean • Boston, MA

2d • Remote

About The Position

The Forward Deployed Applied Scientist II (Agentic AI) works at the intersection of AI research, production deployment, and customer impact. The role embeds with high-growth startups, technology innovators, and AI-native enterprise partners to design, build, and deploy custom autonomous agent architectures on DigitalOcean’s AI infrastructure. It combines direct customer integration, rapid prototyping, production hardening, platform validation, and feedback to DigitalOcean product and research teams. The role is remote and may require travel up to 30% for customer engagements, strategic 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.
• Strong customer-facing engineering skills, empathy, and clear technical communication, including explaining complex AI trade-offs to engineering leads and executive sponsors.
• A bias for action, ownership of the customer experience, and an ability to work in a big, bold, and scrappy manner.
• 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.
• 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 directly with client teams, including CTOs and AI leads.
• Comfort working in fast-moving environments and tuning model workloads for diverse accelerator architectures.
• Experience collaborating with customers, model vendors, or ecosystem partners on benchmarking, optimization, fine-tuning, or launch readiness initiatives.

Responsibilities

• Design and implement production-ready multi-agent workflows, autonomous reasoning loops, and tool-augmented LLM architectures to solve customer challenges.
• Work directly with technology innovators, CTOs, and AI engineering leads to translate ambiguous business requirements into high-performance, deterministic AI and agentic workflows.
• Prototype agentic frameworks, including LangGraph, AutoGen, or custom execution graphs, and harden 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 feedback with AI/ML product teams and translate deployment friction into platform enhancements.
• Validate DigitalOcean AI-native platform capabilities, including the Inference Engine, runtimes, orchestration systems, GPU platforms, and deployment workflows.
• Surface 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.

Benefits

• 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.
• Potential bonus based on company and individual performance.
• Equity compensation for eligible employees, including equity grants upon hire.
• Option to participate in the Employee Stock Purchase Program.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed