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About This Role
Lumenci is looking for a Customer Success \& GTM Associate – AI Platform to help launch and scale Lumenci AI, our next\-generation AI\-powered patent intelligence platform. Based in Austin, TX, this role is ideal for professionals with 2–5 years of experience in customer success, enterprise Legal SaaS, or B2B technology who want to work at the forefront of AI and legal tech. You will play a key role in driving customer onboarding, supporting pilot programs, and accelerating adoption across law firms, patent investors, and corporate IP teams while helping shape how AI products are brought to market and scaled.
About Lumenci
Founded in 2018, Lumenci is a premier intellectual property consulting firm specializing in patent litigation, expert witness testimony, and patent monetization for leading corporations and global law firms operating across the software, telecommunications, semiconductors, and emerging technology sectors.
A key part of Lumenci’s model is the integration of deep technical expertise with litigation strategy. Our teams support high\-stakes disputes by developing technical narratives, performing rigorous analysis, and partnering with world\-class expert witnesses whose testimony shapes case outcomes in courts and arbitration forums worldwide.
Headquartered in Austin, Texas, with offices in San Francisco, New York, and Gurugram, India, Lumenci delivers end\-to\-end support spanning technical analysis, expert strategy, valuation, and monetization execution. Our differentiated approach, which includes combining engineering depth, litigation experience, and operational rigor, has resulted in long\-term partnerships with leading law firms and technology companies navigating complex IP disputes. Lumenci is backed by VSS Capital Partners (“VSS”) and Century Equity Partners (“CEP”). VSS is a private equity firm dedicated to investing in tech\-enabled business services, healthcare, and education companies. Since 1987, VSS has managed eight private capital funds with aggregate committed capital of nearly $4 billion across 103 platform companies and over 600 add\-on acquisitions.
VSS’s investment in Lumenci was from VSS Structured Capital IV, L.P. (“VSS SC IV”), a $530 million fund which had its final closing in December 2022\. CEP is a private equity firm headquartered in Boston, MA, that partners with companies seeking investments to support growth or fund acquisitions, partial buyouts, or recapitalization opportunities.
Employment Type: Full\-time, exempt
Work Arrangement: Hybrid (possibility of remote with intention to relocate)
Location: Austin, TX
Reports To: Customer Success \& GTM Associate– AI Platform
Role Overview
Lumenci is seeking a Customer Success \& GTM Associate – AI Platform to support the launch, commercialization, and adoption of Lumenci AI across law firms, patent investors, and corporate IP teams. This role is designed for an early\-career professional with 2–5 years of experience who wants to grow at the intersection of enterprise Legal SaaS, AI products, customer success, and go\-to\-market execution. The Customer Success \& GTM Associate will work closely with the Customer Success \& GTM Lead, Vice President of Product, and cross\-functional teams to help onboard customers, support pilot programs, collect customer insights, and execute repeatable customer success motions for Lumenci’s AI\-powered patent intelligence platform.
Key Responsibilities
Customer Success and Platform Onboarding
- Support onboarding of law firms, enterprise customers, and early AI platform adopters under the guidance of the Customer Success \& GTM Lead.
- Assist customers in navigating AI\-powered product workflows and implementation processes.
- Help drive platform adoption by responding to customer questions, documenting best practices, and escalating issues as needed.
- Capture and organize customer feedback, workflow challenges, and adoption barriers for review by Product and Customer Success leadership.
Pilot Program Support and Product Commercialization
- Assist in coordinating Lumenci AI pilot programs, including scheduling, customer communication, and tracking deliverables.
- Help structure pilot documentation, including success criteria, timelines, and enablement materials.
- Collect usage metrics and qualitative feedback from pilot customers and summarize insights for Product and GTM stakeholders.
- Contribute to playbooks and templates that support scalable onboarding and customer success programs.
Go\-To\-Market Execution and Customer Engagement
- Support go\-to\-market initiatives such as building early adopter lists, preparing customer\-facing presentations, and coordinating product demos.
- Partner with Sales, Product, and Marketing to ensure consistent messaging and positioning for Lumenci AI.
- Help maintain CRM and customer tracking data related to pilots, onboarding status, and adoption milestones.
Customer Discovery and Market Intelligence
- Participate in structured customer discovery calls, taking notes and documenting key pain points and workflow needs.
- Help monitor competing legal tech, AI, and enterprise Legal SaaS offerings and summarize relevant insights for the Product team.
- Assist in organizing customer and market feedback to inform product roadmap discussions.
Revenue and Expansion Support
- Track pilot outcomes and support conversion of successful pilots into recurring subscription or usage\-based revenue opportunities.
- Help identify upsell and expansion signals and route them to the Customer Success \& GTM Lead and Sales.
- Maintain basic dashboards or reports that show customer health indicators, adoption metrics, and expansion opportunities.
Who You Are
- Early\-career, customer\-focused professional who enjoys working with technology products and solving problems for customers.
- Curious about AI and legal technology, comfortable working with data and workflows, and eager to learn how enterprise software is adopted in complex environments.
- Collaborative team member who enjoys working across product, engineering, consulting, and commercial teams in a fast\-paced setting.
Required Qualifications
- 2–5 years of experience in one or more of the following areas:
- Customer success or account management
- Enterprise SaaS or software implementation
- Product operations, sales engineering, or revenue operations
- Consulting, professional services, or business operations
- Exposure to B2B software, AI/ML products, or legal / IP technology is a plus.
- Strong written and verbal communication skills, with comfort interacting with external customers and internal stakeholders.
- Ability to manage multiple tasks, follow structured processes, and maintain accurate documentation.
- Analytical mindset with the ability to work with basic product usage data, customer feedback, and workflow metrics.
- Comfortable working in a fast\-paced, evolving product environment with a high degree of ownership.
Preferred Qualifications
- Experience in one or more of the following areas is helpful but not required:
- Customer success or onboarding for Legal SaaS products
- Legal technology, IP analytics, or adjacent professional services
- Workflow automation, AI\-enabled tools, or data\-driven products
- Familiarity with CRM tools such as Salesforce or HubSpot and collaboration tools such as Jira, Confluence, or similar platforms.
Key Traits for Success
- Highly customer\-centric and service\-oriented.
- Organized and detail\-oriented, with strong follow\-through.
- Able to learn new technical concepts and translate them into practical steps for customers.
- Collaborative and low\-ego, able to work across product, engineering, consulting, and business teams.
- Proactive and resourceful, with a growth mindset.
What You Will Help Build
You will help shape and scale Lumenci AI into a category\-defining enterprise AI platform for the intellectual property ecosystem.
This includes helping drive:
- Enterprise adoption of AI\-powered patent analytics workflows
- Scalable onboarding and customer success programs
- Product\-market fit across law firms, patent investors, and corporate IP teams
- Commercialization of next\-generation AI solutions for patent monetization and IP intelligence
- Repeatable enterprise AI adoption and customer engagement strategies
- The future of AI\-enabled workflows across the broader IP ecosystem
Why you will love working for Lumenci:
Be Part of a Global Team: Joining Lumenci means joining a diverse and globally distributed team. You'll collaborate with talented individuals from different backgrounds and cultures, bringing unique perspectives to every project.
Growth and Development Opportunities: At Lumenci, we believe in recognizing and rewarding merit. We offer opportunities for merit\-based promotions, allowing you to advance your career based on your performance, contributions, and dedication. We are committed to supporting your professional growth and development, providing the resources and mentorship needed to excel in your role and take on new challenges.
Benefits \& Total Rewards* Comprehensive Health \& Well\-being: We offer robust medical, dental, and vision insurance options that include significant company contributions toward employee premiums to support the health of our "Luminaries" and their families.
- Retirement Strategy: Lumenci provides a 401(k) program with a competitive employer\-matching contribution, enabling employees to build long\-term financial security.
- Performance\-Based Incentives: Our total cash compensation includes a variable pay component designed to reward both individual achievements (KRAs) and overall company success.
- Time Off \& Leaves: We prioritize work\-life harmony through a balanced leave policy that includes accrued Paid Time Off (PTO), designated company holidays, and additional "floating" holidays for personal or cultural significance.
- Holistic Wellness Support: Beyond standard medical care, we provide dedicated Wellness Leave to address both physical health and emotional well\-being, as well as paid leave for significant life events like bereavement and parental bonding.
- Culture of Autonomy \& Growth: Employees benefit from a flexible, remote\-first work environment that favors autonomy and provides structured support for ongoing professional development and career advancement.
Compensation and Pay Transparency
The expected total annual compensation range for this role is $80,000–$115,000, which may include base compensation and other eligible incentive components.
Actual compensation will depend on skills, experience, and work location. For candidates in jurisdictions that require pay range disclosures, Lumenci will provide a good\-faith compensation range for the candidate’s location as part of the job posting or during the interview process, consistent with applicable law.
Salary History: In compliance with applicable laws, Lumenci does not request, require, or rely on an applicant’s prior salary history when determining a starting salary or during any part of the hiring process.
Equal Opportunity Employer: Lumenci is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.
Accommodation: If you require a reasonable accommodation to complete this application or participate in the interview process, please let your recruiter know.
Employment Relationship: Nothing in this job description creates or is intended to create a contract of employment for any specific period of time. Employment with Lumenci, if offered, is on an at\-will basis and may be terminated by either the employee or Lumenci at any time, with or without cause and with or without notice, subject to applicable law.
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Salary Context
This $80K-$115K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1616 roles with salary data).
View full AI/ML Engineer salary data →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 3,057 AI roles we're tracking, AI/ML Engineer positions make up 72% of the market. At Lumenci, 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 Required
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 $179,000 based on 11,905 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,380. This role's midpoint ($97K) sits 46% below the category median. Disclosed range: $80K to $115K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Lumenci AI Hiring
Lumenci has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US. Compensation range: $115K - $115K.
Location Context
AI roles in Austin pay a median of $218,800 across 493 tracked positions. That's 9% above the national median.
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 3,057 open positions tracked in our dataset. By seniority: 94 entry-level, 1,467 mid-level, 1,148 senior, and 348 leadership roles (Director, VP, C-Level). Remote roles make up 17% of the market (513 positions). The remaining 2,528 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,057 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,189), Data Scientist (233), AI Software Engineer (195). 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 (94) are outnumbered by mid-level (1,467) and senior (1,148) 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 348 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 17% of all AI roles (513 positions), with 2,528 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,566 postings), Aws (974 postings), Azure (725 postings), Rag (683 postings), Gcp (597 postings), Prompt Engineering (472 postings), Pytorch (461 postings), Claude (447 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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