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General Manager, Head of Growth, Zlayt AI
About Zlayt AI
Zlayt AI is a litigation automation platform built exclusively for personal injury law. From demand letters and deposition summaries to discovery responses and case analysis, Zlayt equips law firms with intelligent tools that draft faster, think strategically, and deliver results without friction, setup, or compromise.
We're an early\-stage, product\-led company building the category\-defining GTM motion for legal AI in a large, underserved niche. This is a rare opportunity to build the commercial engine, strategy, sales, marketing, and customer success from the ground up, working directly with the founding team.
The Role
Zlayt AI is looking for a General Manager, Head of Growth to own go\-to\-market strategy end\-to\-end and build the initial commercial function across Sales, Marketing, and Customer Success. This is a hands\-on, builder role: you'll set the GTM strategy *and* execute it personally in the early days, then hire and scale the team as traction grows.
Initially, the General Manager will operate across strategy, go\-to\-market, customer success, partnerships, and business operations, working directly with the CEO to build the company from the ground up. As Zlayt AI scales, the role will increasingly focus on leading and growing the commercial organization while maintaining accountability for overall business performance
You'll report directly to the CEO/founding team and be the most senior commercial hire in the company, with a mandate to define how Zlayt wins, retains, and grows law firm customers in a specialized, relationship\-driven vertical (legaltech/SaaS for personal injury law).
This role is ideal for someone who has operated in early\-stage SaaS, understands vertical/niche B2B sales motions, and wants ownership over building a GTM org rather than inheriting one.
What You'll Own:
GTM Strategy
- Define and continuously refine Zlayt's go\-to\-market strategy: target segments, ideal customer profile (ICP), pricing/packaging input, channel strategy, and competitive positioning within the personal injury legal tech market.
- Translate product capabilities (demand letters, deposition summaries, discovery responses, case analysis) into a clear value proposition and messaging framework for law firm buyers and users.
- Set GTM goals, pipeline targets, and success metrics; report progress and market insights directly to the founding team/board.
Sales
- Build and run the sales motion from first principles — outbound/inbound pipeline generation, demos, trials, negotiation, and close, initially as the primary seller.
- Design the sales process, CRM/tooling stack, and qualification criteria appropriate for a niche legal buyer (attorneys, firm partners, office managers).
- Hire, onboard, and manage the first sales hires as volume grows.
Marketing
- Own top\-of\-funnel strategy: positioning, messaging, content, and channel selection (e.g., legal industry events, referral/network\-driven channels, digital demand generation) suited to a specialized legal audience.
- Partner with or manage contractors/agencies as needed until in\-house marketing hires are justified.
- Build early brand and category awareness within the personal injury legal community.
Customer Success \& Retention
- Design the onboarding, adoption, and renewal/expansion motion to ensure law firms see fast time\-to\-value and become long\-term reference customers.
- Own retention and expansion metrics (NRR, churn, usage/adoption) and feed customer insight back into product and sales strategy.
- Build repeatable playbooks for onboarding and account growth as the customer base scales.
Team Building
- Recruit, hire, and develop the first Sales, Marketing, and CS team members as the function scales beyond a single leader.
- Establish the operating cadence, tooling, and reporting rhythm for the commercial org.
What We're Looking For
- 5\+ years in GTM, revenue, sales, or growth leadership roles, with meaningful time in early\-stage/seed B2B SaaS (comfortable operating without established playbooks or large teams).
- Demonstrated experience owning GTM strategy and personally executing it — this is not a role for a pure strategist or a pure individual contributor; it requires both.
- Experience selling into a specialized, relationship\-driven, or regulated vertical (legal, healthcare, financial services, or similar) is strongly preferred; direct legaltech experience is a plus.
- Track record of building repeatable sales and/or customer success motions from scratch, including tooling (CRM, sales engagement, support/CS platforms).
- Comfort working directly with founders in a fast\-moving, resource\-constrained environment; bias toward action over process.
- Strong written and verbal communication skills; ability to credibly speak to legal industry buyers (attorneys, firm operators).
- Data\-driven approach to pipeline, forecasting, and retention metrics.
Nice to have
- Existing network or credibility within the personal injury law or broader legal industry.
- Experience scaling a GTM function from founder\-led sales to a repeatable, team\-based motion.
- Familiarity with AI/automation products and how to sell "trust" and accuracy to a skeptical, high\-stakes buyer.
Success Looks Like (First 12 Months)
- A documented, validated GTM strategy (ICP, positioning, pricing input, channel mix) grounded in real market feedback.
- A repeatable, documented sales process generating predictable pipeline and closed revenue.
- Early customer base with strong onboarding, adoption, and retention (low churn, expansion where possible).
- The foundation of a GTM team (first 1–3 hires across sales/marketing/CS) with clear roles and playbooks.
- Direct, trusted partnership with the founding team on commercial strategy and board\-level reporting.
Reporting Structure
Reports to: CEO / Founding Team: Individual contributor initially, with a mandate and budget to hire as revenue and traction justify it.
Compensation \& Benefits
- Salary commensurate with experience
- Paid time off (20 days) and 12 paid holidays
- Medical, vision, and dental insurance (100% of base medical plan covered by the firm)
- Simple IRA with up to 3% company match
- Ongoing training and mentoring opportunities
Our Core Principles
- Excellence Always
- Only the Best
- Failure Is Not an Option
- We Outwork Our Opponents
- We All Take Out the Trash
- Run the Firm Like a Business
Firm Philosophy
We firmly uphold the value of every individual within our team, ensuring they have the chance to build a rewarding career both financially and personally. Our firm's structure is designed to offer exceptional prospects for growth and advancement to our attorneys. From the initial intake to final verdict, each attorney at our firm handles cases, benefiting from continuous training and guidance from our exceptional team. At Zinda Law Group, every team member leaves at the end of the day with the satisfaction of knowing they have diligently served our clients and positively impacted the lives of others. Join us and experience a remarkable work environment at our law firm.
Zinda Law Group is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status, or other protected status as required by applicable law.
By submitting this application, I understand Zinda Law Group may use review publicly available information about me in order to assess my suitability for employment.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Zinda Law Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Zinda Law Group AI Hiring
Zinda Law Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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