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Reflection

Member of Technical Staff - Safety Lead

London
Posted 3 months ago
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Member of Technical Staff - Safety Lead

Our Mission

Reflection’s mission is to build open superintelligence and make it accessible to all.

We’re developing open weight models for individuals, agents, enterprises, and even nation states. Our team of AI researchers and company builders come from DeepMind, OpenAI, Google Brain, Meta, Character.AI, Anthropic and beyond.

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.

Reasons to use Rodeo

I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?

Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.

Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.

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Graduate Consultant — 2026 Scheme

PwC·London, UK
£35,000/yr

Why you're a good match

Strong

Your economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.

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Why you're a good match

You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.

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Strong

Experience fit

Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.

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Strong

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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 build superintelligence that is truly open, you need to start at the foundation. Joining Reflection means building from the ground up as part of a small talent-dense team. You will help define our future as a company, and help define the frontier of open foundational models.

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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 the best talent globally. Health & wellness: Comprehensive medical, dental, vision, life, and disability insurance. Life & family: Fully paid parental leave for all new parents, including adoptive and surrogate journeys. Financial support for family planning. Benefits & balance: paid time off when you need it, relocation support, and more perks that optimize your time. Opportunities to connect with teammates: lunch and dinner are provided daily. We have regular off-sites and team celebrations.

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Skills

Computer Science
Machine Learning
AI Safety
Adversarial Attacks
Red-Teaming Methodologies
Interpretability
Software Engineering
Automated Evaluation Pipelines
Large-Scale ML Systems
Reinforcement Learning
Model Safety
Model Alignment
Dynamic Adversarial Testing
Jailbreaking Techniques
Defenses

Location

London, England, United Kingdom

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