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.

What You'll Do

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

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 workstreams, 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+ years of relevant experience delivering technical systems, including 2+ years in a technical leadership role (e.g., tech lead, engineering manager or founding engineer).

Demonstrated ability and interest in customer-facing work: understanding user needs, architecting solutions for real business problems, and delivering tangible outcomes. You can go from a whiteboard session to debugging a deployment in the customer’s environment.

Self-starter with high agency and ownership who excels in fast-paced startup environments where the playbooks are still being written.

What We Offer

We believe that to make intelligence open and accessible to all, you need to start at the foundation. Joining Reflection means building from the ground up as part of a talent-dense team. You will help define our future as a company, and help define the future of open foundational models.