Reward & Recognition Partner, 12 months Fixed Term Contract (Basé à London)

Jobleads
Greater London
1 month ago
Applications closed

Related Jobs

View all jobs

Genomic Data Scientist

Practice Manager - Data Engineering

Senior Analytics Data Engineer

Data Engineer

Pensions System Calculation and Data Analyst

Energy Forecast Lead

At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.

Snapshot

This role sits within the Reward & Recognition team; a current team of six based in London and the US, embedded within our wider People & Culture team. You will be managed by a US-based Partner, and collaborate closely with the global team. This position is available on a fixed term contract of 12 months.

About us

Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.

The role

You’ll have responsibility for delivering processes and programs in North America, with a focus of working closely with our business leaders and talent acquisition teams.

Key responsibilities

  • Provide advice and counsel to the Talent Acquisition team and hiring managers through the offer negotiation process.
  • Work with compensation metrics, reports, and tools to inform compensation decisions and forecast, report, and/or analyse compensation outcomes.
  • Communicate effectively with senior leaders and people & culture teams to provide insights to enable insights-led business decisions.
  • Contribute to running our compensation programs that support our wider organisational philosophy while ensuring strong governance.
  • Develop and implement reward and recognition frameworks for the full organisational structure at DeepMind.
  • Lead compensation projects from design through execution, including modelling, project planning, communicating, and training in partnership with the rest of the Reward & Recognition team.
  • Continuously evaluate the efficiency of compensation programs, innovating and recommending changes to improve the effectiveness of our investment in total rewards.

About you

In order to set you up for success as a Reward & Recognition Partner at Google DeepMind, we look for the following skills and experience:

  • Experience working on compensation in a tech company, preferably with a machine learning focus.
  • Knowledge of competitive talent environment in California and US regulations.
  • Experience of working on complex offers and balancing tensions between hiring needs and longer-term compensation implications.
  • You are comfortable working autonomously and making decisions under pressure.
  • Ability to assimilate ambiguous information from a wide variety of sources to provide meaningful and timely insights to inform decisions, and you are comfortable communicating these findings.
  • You can run with a brief and are comfortable dealing with some ambiguity. When something you are working on changes, you adapt comfortably with a readiness to learn.
  • You are proficient using Google Sheets, with strong analytical skills.
  • You are collaborative in your approach to stakeholder management, and have experience of owning relationships with senior stakeholders. You are highly organised with experience of managing your own workload across an array of activity and projects.
  • You have experience partnering with other teams and have good communication skills, with a natural ability to build relationships across an organisation, instilling trust and creating understanding.

The US base salary range for this full-time position is between $111,000 - $170,000 + bonus + benefits. Your recruiter can share more about the specific salary range for your targeted location during the hiring process.

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.