Spellbook

Spellbook

Account Executive, Commercial In-House

Remote - Canada · Mid

Sponsorship not specified$10k-$50kDetected 230 days ago
HubSpotCRMSalesAccount ManagementPipeline ManagementNegotiationExcelRecruitingCommunicationMentoring

About the role

  • This role is ideal for someone who excels in consultative selling and managing multi-threaded deals.

Responsibilities

  • Manage deals through a longer, multi-step sales cycle (4-8 weeks+), from qualification to close.
  • Maintain accurate and timely data in our CRM system to ensure our records are up-to-date.
  • Collaborate within and outside of the Sales team to share knowledge, learnings and continuously improve sales practices.
  • Perform any additional tasks assigned by management that contribute to the improvement of the sales team or achievement of revenue goals, including but not limited to attending events/conferences or hosting internal training sessions.
  • As an Account Executive on our Commercial In-House team, you will manage more complex SMB deals at the edge of mid-market, engaging multiple stakeholders and navigating longer sales cycles.

Requirements

  • 3+ years of quota-carrying sales experience, ideally in SaaS or B2B environments.
  • Strong consultative selling skills with the ability to uncover deeper customer needs.
  • Driven by a desire to excel with a track record of exceptional performance.
  • Experience selling into SMB or mid-market companies with in-house legal teams.
  • Familiarity with the legal tech space, understanding its unique challenges and opportunities.
  • A self-starter who excels at prospecting, managing pipelines, and closing deals.
  • Be articulate and persuasive, with the ability to deliver compelling pitches and handle objections with ease.
  • Proven ability to manage sales cycles with deal sizes in the ~$10K-$50K range.
  • Proficient with tools like HubSpot, or equivalent for pipeline management and forecasting.
  • Committed to building a successful career in sales
  • Demonstrates a growth mindset by actively seeking, accepting, and applying regular coaching and feedback to continuously improve performance and skills.
  • Able to thrive in an unstructured environment, embracing the freedom to determine your best working style.
  • A proactive mindset with a willingness to take initiative and contribute to team goals.
  • Effective verbal and written communication skills and ability to collaborate effectively within a distributed team.

Compensation

  • Spellbook uses industry benchmark data to establish compensation bands for all roles.
  • The salary range listed for a position reflects the expected total wage range for the role-including base salary and on-target commissions, where applicable-and may span multiple career levels.
  • Final compensation is determined during the interview process based on factors such as experience, skills, scope, and role level.
  • In addition to base salary and applicable commissions, total rewards may include equity, health and wellness benefits, and other company programs.
  • Full details will be shared during the interview process.

Company info

  • Spellbook is the most comprehensive AI copilot for transactional lawyers.
  • It works directly inside Microsoft Word to help legal teams draft, review, and negotiate contracts up to 10x faster and with greater precision.
  • Today, more than 4,000 law firms, in-house teams, and solo practitioners rely on Spellbook to simplify their workflows and eliminate the drudgery of everyday contract work.
  • We are backed by leading investors including Khosla Ventures, Thomson Reuters Ventures, Inovia Capital, The LegalTech Fund, Bling Capital, and Moxxie Ventures.
  • The company recently raised $50 million in Series B funding, led by Keith Rabois at Khosla Ventures, bringing its total funding to more than $80 million.
  • This is an existing vacancy
  • Work cross-functionally with IT to complete any RFP or security questionnaires as part of the sales cycle.

This listing is sourced directly from Spellbook's careers page and normalized into a canonical job model.