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Salary
$209k – $362k per year (Estimated)
Location
In office (San Francisco, London, New York)
Seniority
Staff
Employment
Full-Time
Overview
Company
Impact
Profile match
Reflection AI is a company founded in 2024 by former Google DeepMind researchers who worked on AlphaGo and large language models. It builds autonomous coding agents and has committed to releasing frontier open-weight models as an American counterweight to Chinese open model releases. The company raised a very large round in 2025 to fund training at frontier scale.

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.

About the Role

  • Own the red-teaming and adversarial evaluation pipeline for Reflection’s models, continuously probing for failure modes across security, misuse, and alignment gaps.

  • Work hand-in-hand with the Alignment team to translate safety findings into concrete guardrails, ensuring models behave reliably under stress and adhere to deployment policies.

  • Validate that every release meets the lab’s risk thresholds before it ships, serving as a critical gatekeeper for our open weight releases.

  • Develop scalable, automated safety benchmarks that evolve alongside our model capabilities, moving beyond static datasets to dynamic adversarial testing.

  • Research and implement state-of-the-art jailbreaking techniques and defenses to stay ahead of potential vulnerabilities in the wild.

About You

  • Graduate degree (MS or PhD) in Computer Science, Machine Learning, or related discipline, or equivalent practical experience in AI Safety.

  • Deep technical understanding of LLM safety, including adversarial attacks, red-teaming methodologies, and interpretability.

  • Strong software engineering capabilities with experience building automated evaluation pipelines or large-scale ML systems.

  • Experience with Reinforcement Learning (RLHF/RLAIF) and how it impacts model safety and alignment is a strong plus.

  • Thrive in a fast-paced, high-agency startup environment with bias toward action.

  • Willing to make high-stakes decisions regarding model release and safety thresholds.

  • Passionate about advancing the frontier of intelligence.

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.

We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported.

  • Top-tier compensation: Salary and equity structured to recognize and retain our talent globally.

  • Stock options: Everyone who joins and contributes to Reflection's success gets to share in the upside through stock options.

  • Health & wellness: Comprehensive medical, dental, vision, and life, with an annual wellness allowance.

  • Meals: Lunch and dinner are provided in the office daily.

  • Life & family: 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys.

  • Vacation days: Unlimited paid time off in the U.S. and 30 days in the U.K.

  • Sponsorship support: We sponsor visas to help exceptional talent join our team and support long-term immigration pathways where applicable.

  • Team building: We have regular off-sites, happy hours, and team celebrations.

Export Control Notice: This position may require access to technology or source code subject to the U.S. Export Administration Regulations. Any offer of employment for this role may be conditioned on the Company's ability to provide the candidate with access to such technology or source code in compliance with applicable U.S. export control laws, which may require the Company to seek government authorization.

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