# Member of Deployment Staff - Public Sector at Reflection AI

Reflection AI is hiring a Member of Deployment Staff - Public Sector in New York, United States. Level rates it Works on AI ●●●○; you can [apply on Reflection AI](https://jobsbylevel.com/go/145cd9d0-e70f-47c9-b029-10d210114485?utm_medium=ai-agent).

AI Level 3, AI centrality 70 out of 100. New York, NY.

## Details

- Company: [Reflection AI](https://jobsbylevel.com/companies/reflection-ai)
- AI level: AI Level 3 (score 70 out of 100)
- Location: New York, NY
- Posted: November 3, 2026
- Apply: https://jobsbylevel.com/go/145cd9d0-e70f-47c9-b029-10d210114485?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=member-of-deployment-staff-public-sector-at-reflection-ai-20e8f9

## Prepare for this job

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## Description

Our Mission Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all. Role Overview Members of Deployment Staff are Reflection’s technical owners in every customer engagement. You will own the technical execution of strategic engagements end to end, from discovery and solution architecture through proof-of-concept and production delivery. The work spans every layer of the AI stack: GPU orchestration, inference, model post-training, and the custom harnesses and applications built on top. We’re seeking exceptional people with depth across the AI stack who can partner with customer’s ML and engineering teams, win and deliver strategic engagements, and turn what they learn in the field into input that shapes our models and platform. In this role, you will support U.S. government customers, deploying Reflection’s open models into secure, classified, or edge environments. This position requires an active U.S. government clearance (TS/SCI preferred). What You'll Do Secure Deployment: Design, deploy, and operate agentic systems in air-gapped and classified networks, including fully offline model serving without access to external services. Mission Engagement: Work on-site with government customers, including in SCIFs. Turn mission requirements into secure AI solutions that meet accreditation and compliance standards. Technical Discovery & Architecture: Lead deep technical discovery with customer teams across inference workloads, agentic applications and post-training. Understand customer needs and translate them into feasible technical solutions with clear success criteria to make Reflection the technical winner in every deal. Benchmarks & POCs: Own proofs of concept end to end: define evaluation criteria with the customer, benchmark on their real workloads, and demonstrate Reflection’s quality, latency, and cost against competing options. Delivery: Architect and build complex AI systems, including agentic applications, model-serving infrastructure, evaluations, and post-training recipes . Own the customer engagement and relationship: define project plans, sequence and manage work across workstreams, and keep customer stakeholders aligned. Product & Research Contribution: Identify repeatable patterns and model gaps in the field that should shape our platform or model capabilities. Build prototypes, validate that they meet customer needs, and generalize them into Reflection’s platform and research priorities. Playbooks & Operating Model: Help shape and scale the deployment organization by defining playbooks, reference architectures, reusable technical assets and best practices that make every teammate more effective and support the team’s growth. What We're Looking For Experience delivering software into classified or air-gapped government environments: you know the security controls, the accreditation processes, and the limits on offline deployment. Strong technical fluency: comfortable reading, writing and reasoning about code, architecture and system behavior, with strong Python and experience shipping production-grade systems. A track record of leading complex technical projects: coordinating contributors across work-streams, making practical tradeoffs, removing obstacles, and owning end-to-end delivery outcomes. Hands-on infrastructure and inference experience: you’ve deployed multi-node GPU clusters (Kubernetes or Slurm), served large models in production (vLLM, SGLang), and rigorously benchmarked tradeoffs like quantization, caching, batching and speculative decoding. Agent engineering: you’ve shipped LLM-based agents to production in the last 1–2 years, designed their harnesses (tool calling and MCP, context management, orchestration, guardrails) and the evals behind them, and know when post-training can be used. 5+

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