DigitalOcean Logo

DigitalOcean

Forward Deployed Applied Scientist I (Agentic AI)

Posted Yesterday
Hybrid
Seattle, WA
139K-174K Annually
Mid level
Hybrid
Seattle, WA
139K-174K Annually
Mid level
Design, deploy, and optimize production-ready autonomous agent systems for customer applications. Build multi-agent workflows, tool-augmented LLM architectures, evaluation frameworks, safety guardrails, and persistent memory systems. Collaborate directly with startups, CTOs, and AI leaders to translate business needs into reliable AI solutions. Validate DigitalOcean’s AI infrastructure, identify platform improvements, and communicate deployment insights to product and engineering teams. Travel up to 30% for customer engagements and collaboration.
The summary above was generated by AI

Dive in and do the best work of your career at DigitalOcean. Journey alongside a strong community of top talent who are relentless in their drive to build the simplest scalable cloud. If you have a growth mindset, naturally like to think big and bold, and are energized by the fast-paced environment of a true industry disruptor, you’ll find your place here.  We value winning together—while learning, having fun, and making a profound difference for the dreamers and builders in the world. 

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
  • 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.
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.

Compensation Range: 
  • $139,200.00 - $174,000.00

*This is a hybrid role

JR: 2026-8295

#LI-Hybrid


Why You’ll Like Working for DigitalOcean
  • We innovate with purpose. You’ll be a part of a cutting-edge technology company with an upward trajectory, who are proud to simplify cloud and AI so builders can spend more time creating software that changes the world. As a member of the team, you will be a Shark who thinks big, bold, and scrappy, like an owner with a bias for action and a powerful sense of responsibility for customers, products, employees, and decisions.
  • We prioritize career development. At DO, you’ll do the best work of your career. You will work with some of the smartest and most interesting people in the industry. We are a high-performance organization that will always challenge you to think big. Our organizational development team will provide you with resources to ensure you keep growing. We provide employees with reimbursement for relevant conferences, training, and education. All employees have access to LinkedIn Learning's 10,000+ courses to support their continued growth and development.
  • We care about your well-being. Regardless of your location, we will provide you with a competitive array of benefits to support you from our Employee Assistance Program to Local Employee Meetups to flexible time off policy, to name a few. While the philosophy around our benefits is the same worldwide, specific benefits may vary based on local regulations and preferences.
  • We reward our employees. The salary range for this position is based on market data, relevant years of experience, and skills. You may qualify for a bonus in addition to base salary; bonus amounts are determined based on company and individual performance. We also provide equity compensation to eligible employees, including equity grants upon hire and the option to participate in our Employee Stock Purchase Program.
  • DigitalOcean is an equal-opportunity employer. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.

Application Limit: You may apply to a maximum of 3 positions within any 180-day period. This policy promotes better role-candidate matching and encourages thoughtful applications where your qualifications align most strongly.

Similar Jobs at DigitalOcean

An Hour Ago
Hybrid
250K-312K Annually
Expert/Leader
250K-312K Annually
Expert/Leader
Artificial Intelligence • Cloud • Software • Infrastructure as a Service (IaaS)
Own the reference architecture and production operation of rack-scale GPU inference systems. Design NVLink domain allocation, RDMA fabrics, NCCL performance, Kubernetes topology-aware scheduling, multi-node serving, failure recovery, diagnostics, fleet lifecycle, and rack validation. Partner with data center teams, NVIDIA, and ODMs on hardware bringup, cooling, power, and deployment constraints. Set technical direction, mentor engineers, and represent the company in industry and customer engagements.
Top Skills: BmcDcgmDraFirmwareGang SchedulingGb300 Nvl72GoGpu InfrastructureGpu OperatorGpudirect RdmaInfinibandKubernetesLeaderworkersetLiquid CoolingNcclNetwork OperatorNumaNvlinkNvswitchPciePythonRdmaRocev2Topology ManagerVera Rubin Nvl144
An Hour Ago
Hybrid
250K-312K Annually
Expert/Leader
250K-312K Annually
Expert/Leader
Artificial Intelligence • Cloud • Software • Infrastructure as a Service (IaaS)
Own the technical vision and roadmap for DigitalOcean’s unified memory and storage layer supporting large-scale LLM inference. Design multi-tier systems across GPU memory, host memory, NVMe, and remote storage; integrate with vLLM, SGLang, and TensorRT-LLM; define KV-cache transfer, eviction, admission, and routing protocols; optimize performance and unit economics; and lead cross-functional architecture, mentorship, open-source contributions, and customer-facing technical initiatives.
Top Skills: C/C++DraDramGoGpu HbmGpudirectKubernetesLlm-DLmcacheMigMpsNvidia DynamoNvlinkNvmeNvme-OfPythonRdmaRustSglangTensorrt-LlmVllm
An Hour Ago
Hybrid
250K-312K Annually
Expert/Leader
250K-312K Annually
Expert/Leader
Artificial Intelligence • Cloud • Software • Infrastructure as a Service (IaaS)
Owns the end-to-end optimization of large language model inference across NVIDIA and AMD GPU fleets. Responsibilities include quantization, GPU kernel development, attention and MoE optimization, speculative decoding, parallelism strategies, benchmarking, regression infrastructure, and upstream contributions to serving frameworks. The role sets technical direction, partners with hardware vendors, mentors engineers, and drives production enablement for newly released models.
Top Skills: AiterAmd Mi300C++Composable KernelCudaCutlassHipHipblasltNsightNvidia BlackwellNvidia HopperPythonPyTorchRocmRocprofSglangTensorrt-LlmTritonVllm

What you need to know about the San Francisco Tech Scene

San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.

Key Facts About San Francisco Tech

  • Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Google, Apple, Salesforce, Meta
  • Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
  • Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
  • Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
  • Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account