Go-To-Market Engineer, Lilt
3 days a week in office
AI solution for enterprise translation and content creation
Be an early applicant
AI solution for enterprise translation and content creation
501-1000 employees
Be an early applicant
3 days a week in office
501-1000 employees
Company mission
To make the world's information accessible to everyone regardless of where they were born or which language they speak.
Role
Who you are
- 3-5 years in B2B SaaS or AI/ML go-to-market engineering, RevOps, or marketing operations, with a track record of building automations that actually ship
- Hands-on Clay expertise. You've built waterfall enrichment, scoring, and routing in Clay (or a comparable tool), and you can take a new use case from idea to live quickly
- Agentic AI and automation fluency. Comfortable proposing, designing, and building agentic workflows and integrations using APIs, webhooks, and orchestration tools, to connect systems and eliminate manual work
- Working proficiency in Python/other languages for building and maintaining AI agent pipelines. Comfortable working inside an existing codebase, not just writing standalone scripts
- Comfortable reasoning about tradeoffs under cost and token constraints. You can explain not just what you built, but why you didn't build the more expensive or more complex version
- Strong Salesforce fundamentals. You can build reports, understand the object model and the automation layer, and use what you find to change how the funnel actually performs
- A point of view on where AI in GTM is heading. We'll ask. We're less interested in whether we agree than in whether you've thought about it seriously
- Obsessed with the metrics that matter: MQLs, pipeline, bookings, funnel conversion, velocity, CAC, and ROI
- Familiarity with marketing automation platforms (HubSpot or similar) a plus
- Ability to build relationships and work cross-functionally
- Smart, curious, and hard-working
- Natural collaborator and communicator, comfortable presenting to executives, boards, and cross-functional stakeholders
What the job involves
- This high-impact, hands-on role is responsible for owning LILT's agentic GTM systems: proposing, designing, building, and maintaining the AI-driven tools and automations that power our go-to-market engine, including our Clay-based enrichment and outbound workflows
- You'll also play a critical role in optimizing our go-to-market technology stack, streamlining our lead-to-opportunity process, and helping to maintain day-to-day department operations
- You'll work closely with GTM leadership across Sales Development, Sales, Marketing, and Operations and Systems functions to drive process optimization and overall efficiency
- You'll recommend, build, and run cross-functional projects and process improvements that directly impact our operational efficiency and top-line growth of all revenue lines
- You'll contribute to the strategy and execution of full-funnel programs that drive predictable, scalable pipeline
- Agentic business systems integration. Propose, design, build, and maintain agentic AI tools and automations that connect and orchestrate our GTM stack end-to-end, reducing manual work and increasing the speed and reliability of our lead-to-revenue motion
- Own Clay and the automation stack. Serve as our resident expert on Clay (and similar enrichment/orchestration tools), building and maintaining tables, waterfalls, and workflows that keep our data accurate and our outbound motion running
- Salesforce lead-to-opportunity. Partner closely with RevOps to own the workflow from a new lead entering the system, through the various lead stages, and converting to an opportunity, including routing, assignment, and the rules that decide who works what
- Full-funnel ownership. Design and optimize the entire lead funnel, from top-of-funnel acquisition through MQL to SQL handoff, driving higher conversion and increased velocity
- Lead and contact data integrity. Work with internal tools and external vendors to maintain a high quality database of contacts across our target industries and personas
- Partner with Sales. Align tightly with revenue leadership to define pipeline goals, campaign targeting, SLAs, and lead management workflows. Sit close enough to the reps to know which parts of the process actually hurt
- Fuel growth. Partner closely with the demand gen team to drive multi-channel campaigns across email marketing, webinars, paid media, SEO, events, outbound motions, and ABM to meet ambitious growth targets
- Use data to scale. Build a data-driven culture. Forecast, measure, and communicate program results, continuously optimizing for ROI and CAC efficiency
- Collaborate cross-functionally. Work across demand gen, product marketing, ADRs, and sales ops to build cohesive, conversion-optimized systems and campaigns
- Department operations. Help with department-level planning for things like managing campaign budget, team meetings and activities, and annual planning
- Documentation and knowledge continuity. Maintain living documentation (Notion) for every agent and workflow you own, to a standard where a new team member or another AI agent could pick up ownership without a live handover call. This is a stated expectation of the role, not an afterthought
- ---Our Stack---
- CRM and RevOps: Salesforce, Default
- Enrichment and orchestration: Clay, ZoomInfo
- Outbound and conversation intelligence: Amplemarket, Trellus
- Inbound and web conversion: Qualified
- Marketing automation: HubSpot
- Analytics: GA4, Salesforce reporting, Looker Studio
- Build environment: Python, LLM APIs, agentic coding tools, Notion for documentation
Salary benchmarks
Our take
Widely available tools like Google Translate are not fit for complex, operationally intense processes that require expert translations. The manual methods of yesterday can't scale to the volume of digital information produced today. Lilt works with companies, schools, and government agencies to provide AI-powered enterprise language translation. Its platform combines a translation management system and neural machine translation with human language experts to create a “human-in-the-loop” approach.
While companies like AI Translate offer intelligent machine translations, they still have linguists fine-tune and correct the texts afterwards. With the Lilt platform, AI and humans work together on text, with the AI making real-time sentence suggestions and the linguist reviewing them immediately. This collaboration delivers 3-5x greater efficiency than traditional human-only translation services, which has aided Lilt's substantial rounds of funding.
Clients such as Asics, Canva, and Digital Ocean use Lilt technology for urgent campaigns and large-scale localisation projects. Recently, Lilt launched its V3.5 translation models, which improve quality by 15% to 20%, significantly enhancing translation accuracy and efficiency. Additionally, Lilt appointed a new Chief Revenue Officer to drive growth and scale, underscoring its commitment to expanding its market presence and further improving its services.
Steph
Company Specialist at Welcome to the Jungle
Insights
2% employee growth in 12 months
Company
Funding (last 2 of 4 rounds)
Apr 2022
$55m
SERIES C
May 2020
$25m
SERIES B
Total funding: $91.9m
Company benefits
- Compensation: Meaningful equity, 401(k) matching, and flexible time off plus company holidays
- Medical Benefits: Employees receive coverage of medical, dental, and vision insurance, plus FSA/DFSA, HSA, and Commuter benefits. In addition, LILT pays for basic life insurance, short-term disability, and long-term disability
- Paid parental leave is provided after 6 months
- Monthly lifestyle benefit stipend via the Fringe platform to allow employees to customize benefits to their lifestyle
Company HQ
Emeryville, CA
Leadership
John DeNero
(Co-Founder & Chief Scientist)Earned a PhD in Computer Science from the University of California, Berkeley. Previously Teaching Professor at UC Berkeley for 7 years and Senior Research Scientist at Google for 4 years.
Spence Green
(CEO)Earned a Ph.D. in Computer Science from Stanford University. Previously Research Assistant - Computer Science at Stanford University for 6 years and Fellow at XSeed Capital for 1 year.
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