Forward Deployed Engineer

Company Overview

The company is an artificial intelligence research organization focused on developing open-source AI models and making advanced intelligence accessible, customizable, and available for broader use. Its work spans foundational model research, AI infrastructure, and practical applications that enable organizations and developers to build and deploy customized AI solutions.

Responsibilities

• Technical Discovery and Architecture
  • Lead technical discovery sessions with customer teams covering inference workloads, agentic applications, and model post-training requirements.
  • Understand customer technical challenges and translate them into practical solution architectures.
  • Define clear technical objectives and success criteria for customer engagements.
  • Demonstrate the technical capabilities and competitive advantages of the company’s AI solutions.
• Benchmarks and Proofs of Concept
  • Lead proof-of-concept projects from initial planning through implementation and evaluation.
  • Establish evaluation criteria and performance benchmarks in collaboration with customers.
  • Test AI models using real customer workloads.
  • Compare model quality, inference latency, and operating costs against alternative solutions.
  • Present benchmark results and demonstrate the suitability of proposed architectures.
• Technical Delivery and Customer Management
  • Architect and develop complex AI systems, including agentic applications, model-serving infrastructure, evaluation frameworks, and post-training workflows.
  • Own technical execution and delivery throughout customer engagements.
  • Develop project plans, coordinate multiple workstreams, and manage implementation timelines.
  • Maintain alignment with customer stakeholders and ensure successful delivery outcomes.
  • Troubleshoot and resolve technical issues within customer environments, from architecture challenges to deployment problems.
• Product and Research Contributions
  • Identify recurring technical requirements, implementation patterns, and model limitations encountered during customer engagements.
  • Develop prototypes and validate solutions against real customer needs.
  • Translate field insights into improvements for platform capabilities, model development, and research priorities.
  • Generalize successful customer solutions into reusable platform features and technical capabilities.

Qualifications

• Strong technical proficiency in reading, writing, and reasoning about code, software architecture, and system behavior.
• Strong Python programming skills.
• Experience developing and deploying production-grade software systems.
• At least 5 years of relevant experience delivering complex technical systems.
• At least 2 years of experience in a technical leadership capacity, such as technical lead, engineering manager, or founding engineer.
• Hands-on experience deploying multi-node GPU clusters using Kubernetes or Slurm.
• Experience serving large machine learning models in production using frameworks such as vLLM or SGLang.
• Strong understanding of inference optimization techniques, including quantization, caching, batching, and speculative decoding.
• Experience deploying LLM-based agents into production within the past 1–2 years.
• Experience designing agent frameworks and supporting components, including tool calling, Model Context Protocol (MCP), context management, orchestration, and guardrails.
• Experience developing evaluation frameworks for AI agents and understanding when model post-training is appropriate.
• Demonstrated ability to lead complex technical projects, coordinate contributors across multiple workstreams, manage tradeoffs, resolve obstacles, and take ownership of delivery outcomes.
• Strong customer-facing communication skills and the ability to understand user requirements, design solutions for business problems, and deliver measurable results.
• Ability to transition effectively between solution design, technical discussions, implementation, and deployment troubleshooting.
• Self-directed approach, strong ownership, and the ability to perform effectively in a fast-paced startup environment with evolving processes.

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