GTM Talent Acquisition Partner

Crane Venture Partners
London
2 months ago
Applications closed

Play a role in the future of softwareDiscover opportunities across our portfolio companiesGTM Talent Acquisition PartnerEncordAt Encord, we're building the AI infrastructure of the future. The biggest challenge AI companies face today is actually not half as glamorous as the outside world may think: it's all about data quality. In fact, the success of any AI application today relies on the quality of a model's training data — and for 95% of teams, this essential step is both the most costly, and the most time-consuming, in getting their product to market. As ex-computer scientists, physicists, and quants, we felt first-hand how the lack of tools to prepare quality training data was impeding the progress of building AI. AI today is what the early days of computing or the internet were like, where the potential of the technology is clear, but the tools and processes surrounding it are still primitive, preventing the next generation of applications. This is why we started Encord. We are a talented and ambitious team of 60, working at the cutting edge of multimodal and visual AI, backed by top investors, including CRV and Y Combinator, leading industry executives like Luc Vincent, former VP of AI at Meta, and other top Bay Area leaders in AI. We are one the fastest growing companies in our space, and consistently rated as the best product in the market by our customers. We have big plans ahead and are looking for our first GTM Talent Acquisition Partner in London to join us in building our team.The RoleYou will be Encord’s first recruiter on the ground in the UK. You’ll be partnering with our co-founders and senior GTM leadership on building out our commercial function – bringing to Encord company-defining talent to sustain the next stage of our hyper-growth. You will guide candidates through the hiring process end-to-end: from sourcing and screening to close, and take the lead across all core commercial functions, including Sales, Growth/Marketing, and Customer Success.About you- You have 2-4 years of experience 1) on a recruiting team within a start-up or corporate environment or 2) on a sales team. - You are focused on one thing: finding top-talent. You know this involves relentless nurturing, creative outbound campaigns, and A/B testing job specs to optimize for inbound. - You are a self-starter and want to be a part of building a hyper growth business from the ground up. - You are target-driven, detail-oriented and have a strong sense of urgency; for each challenge that arises you always find a solution. - When you hear a role and its criteria, you can immediately think of a couple candidates who’d be a great fit for it, whether they’re actively looking or happy in their current roles. - You thrive and get energy from connecting others; you are often the first person top-talent reaches out to when they’re thinking about their next role.More about the Role & EncordThis is a full time role, based in our office in London. Other benefits of Encord: - Strong in-person culture: most of our team is in the office 2-5 days a week. - 25 days annual leave a year + public holidays. - Clear and concrete opportunities to grow – a year ago we were 25 people, now we’re over 60. We’ll be growing insanely fast over the next 24 months and you’ll have all the opportunities for growth that you can handle. Encord offers a unique opportunity to be part of a startup with a clear mission and vision. You will get to explore and build services enterprise AI use cases across many different industry verticals such as healthcare, surveillance, retail, agriculture and many more. Our work is at the cutting edge of computer vision and deep learning, which also includes working on solving unsolved problems within those fields.#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.