Senior Forward Deployed Engineer – AI/ML

🔥 4 minutes ago

🏄 California – Remote

🏄 California – Remote

💵 $167k - $209k / year

⏰ Full Time

🟠 Senior

🤖 AI Engineer

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 0%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

DigitalOcean

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2011

☁️ SaaS

SaaS • Cloud Computing

DigitalOcean is a cloud infrastructure provider that offers a suite of products and services for developers to build, deploy, and scale applications. Their platform provides comprehensive tutorials, reference material, and support documentation to assist users in managing resources effectively using their API and CLI tools. With features like Droplets (virtual machines), managed databases, Kubernetes, and a marketplace for third-party applications, DigitalOcean focuses on simplicity and performance. They cater to both individual developers and larger organizations looking for cloud solutions that are easy to implement and manage.

📋 Description

• Partner with strategic AI-native enterprises and AI startups to architect, deploy, optimize, and scale production AI and agentic systems on DigitalOcean’s AI-Native Cloud • Support complex migrations, production-ready proofs of concept, deployment acceleration, and long-term workload expansion across inference and runtime platforms • Optimize distributed inference and runtime performance through benchmarking, GPU efficiency tuning, KV-cache optimization, speculative decoding, prefill/decode disaggregation, multi-node deployments, and latency/cost optimization • Act as the first customer for DigitalOcean’s AI-native platform capabilities, including Inference Engine, runtimes, orchestration systems, GPU platforms, and deployment workflows • Surface operational insights, architectural gaps, and scaling bottlenecks to Product Engineering and Research teams • Build benchmarking systems, automation tooling, AI starter kits, deployment frameworks, operational playbooks, finetuning workflows, and reference architectures • Collaborate with GPU vendors, model providers, infrastructure partners, and ISVs on co-development, technical validation, optimization, and launch readiness • Enable customer-facing technical teams and partner teams through deployment patterns, benchmarking insights, playbooks, reference architectures, demos, and technical guidance • Travel up to 30% for customer engagements, strategic onsite workshops, ecosystem partnerships, conferences, and internal collaboration • Drive customer adoption, production success, product influence, asset and tooling delivery, field enablement, and ecosystem scale

🎯 Requirements

• Experience designing and operationalizing production AI systems, including inference workloads, agentic runtimes, orchestration frameworks, and AI-native applications • Hands-on experience with inference and serving frameworks such as vLLM, SGLang, Ray Serve, NVIDIA Dynamo, llm-d, or equivalent systems • Experience with LLM optimization techniques including continuous batching, quantization, KV-cache optimization, and speculative decoding • Deep expertise with NVIDIA and AMD GPU platforms and software ecosystems including CUDA, ROCm, TensorRT, Triton, NCCL, RCCL, NVLink, XGMI, and RoCE • Strong proficiency with Kubernetes, distributed systems, networking, storage systems, Infrastructure as Code, and large-scale AI infrastructure architectures • Experience with AI orchestration and agent frameworks such as LangGraph, CrewAI, MCP ecosystems, LlamaIndex, OpenAI Agents SDK, or similar runtime systems • Strong production coding skills in Python or Go • Experience building tooling, automation systems, deployment workflows, benchmarking frameworks, and operational platforms • Proven ability to benchmark and optimize AI infrastructure for scalability, reliability, GPU efficiency, runtime performance, latency optimization, and workload economics • Ability to establish technical credibility with CTOs, Principal architects, Product Engineering teams, and ecosystem partners • 6+ years of experience in Forward Deployed Engineering, ML Engineering, Applied AI Engineering, AI Infrastructure, Technical Consulting, or equivalent customer-facing engineering roles supporting production AI systems • Ability to travel up to 30% for customer engagements, strategic onsite workshops, ecosystem partnerships, 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 • Bonus eligibility based on company and individual performance • Equity compensation, including equity grants upon hire • Option to participate in the Employee Stock Purchase Program