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About This Role
This is a remote role open to candidates currently residing in or willing to self-relocate to the Mid-Atlantic or Northeast United States (Virginia or north).
Ready for a real challenge?
If you're intellectually curious, adaptable, and excited about being at the forefront of AI-driven transformation - not just watching from the sidelines - we want to talk.
Role Overview
GAPVelocity AI is the AI-first modernization business unit of Growth Acceleration Partners (GAP), a leading consulting and technology services provider specializing in custom software, data engineering, and modernization solutions. Our unique Hybrid AI architecture rapidly transforms legacy applications, making us the go-to partner for enterprises aiming to modernize and leverage AI for competitive advantage. The enterprise AI modernization market is experiencing explosive growth with companies seeking to transform legacy systems. Our proprietary Hybrid AI architecture solves what others can’t - enabling rapid, risk-free application migration that competitors can’t touch.
We’re on a mission to scale the Business Unit to $50 million in annual revenue, and we’re searching for consultative and value-driven sales professionals ready to make an immediate and lasting impact.
About the Role: You'll be a critical player in our growth strategy. Our intentional focus for this role is one of a strategic and consultative approach - selling transformative AI-driven migration solutions and complementary GAP Engineering services to enterprise decision-makers.
You'll excel by navigating complex organizations, engaging senior stakeholders, and becoming a trusted advisor who lands and expands within accounts. We're looking for self-starters who can hunt for new opportunities, not just handle inbound leads, and who use persuasion and collaboration rather than confrontation to drive internal and external results.
The ideal candidate brings:
- A track record of enterprise technology sales - specifically selling to CIOs and technology leaders who are making platform and architecture decisions. You've sold solutions where technical debt, platform risk, or application modernization were central to the buyer's decision.
- Hunter DNA - You've built pipeline from scratch, not just inherited accounts. You're energized by prospecting, competitive displacement, and winning net-new logos.
- Comfort with complexity - You've navigated 6-8 month sales cycles involving multiple stakeholders, technical validation, and enterprise buying committees. You know how to quarterback complex deals without losing momentum.
- Technical aptitude - You don't need to be an engineer, but you need to get excited about learning technical concepts. You can grasp how AI transforms legacy code and explain it in business terms. You ask probing questions about how things work, not just what they cost.
- Intellectual flexibility - You've succeeded by adapting your approach to each situation, not by rigidly following the same playbook. You can read the room, pivot your strategy, and figure out what works for each unique buyer.
- 10+ years of proven success - You've consistently hit quota selling complex technology solutions. You can point to specific examples of deals you've won through persistence, creativity, and deep customer understanding.
Bonus points if you have:
- Direct experience selling legacy application modernization or migration platforms/tools (VB6, PowerBuilder, Clarion, COBOL, etc.)
- Background in the Microsoft partner ecosystem or cloud migration partnerships
- Proven success in manufacturing, financial services, or insurance verticals
- Existing relationships with enterprise CIOs at 30+ year old companies
You'll thrive in this role if:
- You're intellectually curious - You ask "how does this actually work?" not just "what's the ROI?" You want to understand the technology deeply enough to have credible conversations with technical executives.
- You adapt and learn quickly - This isn't about executing the same playbook you've run for 10 years. Every enterprise has unique legacy challenges. You'll need to learn on the fly, pivot your approach, and figure things out as you go.
- You're building something, not just selling something - We're establishing a new category in AI-driven modernization. You'll help define how we go to market, not inherit a well-worn path. If you need everything figured out for you, this isn't the role.
- You're genuinely excited about AI - Not because it's a buzzword, but because you see how transformative it is for solving real technical problems. You want to be part of this revolution while it's happening, not read about it later.
- You have grit - These are complex, high-stakes deals with long cycles. You'll face rejection, technical objections, and buying committees. You need the persistence to push through without losing momentum or enthusiasm.
- You bring enterprise discipline - You run a tight sales process (MEDDIC, BANT, or similar), maintain meticulous pipeline hygiene, and can forecast accurately. But you know when to follow the process and when to break it.
Key Responsibilities:
- Execute a consultative sales approach, effectively articulating the strategic value of GAPVelocity AI's AI-driven migration solutions.
- Consistently meet or exceed quarterly and annual revenue targets, playing a pivotal role in driving GAPVelocity AI’s rapid growth trajectory.
- Proactively identify and develop new opportunities, engaging senior decision-makers to secure budget approval and strategic alignment.
- Collaborate with internal teams, including becoming a two-in-a-box champion with Solution Architects, and Delivery manager as well as closely align with Engineering,and Marketing, to provide seamless client experiences and expand account revenue.
- Champion and sell GAP's broader portfolio of services to existing and new clients, maximizing revenue opportunities through upselling and cross-selling.
You’ll be supported by:
- Growth engine generating qualified leads
- Solution Architects providing technical expertise on client calls
- Marketing driven lead generation campaigns and content resources
- CRM and AI sales automation tools
- Ongoing sales training and AI certification programs
Why GAPVelocity AI?
- Join an innovative team leading the charge in AI-driven application migration and modernization.
- Opportunity to drive substantial growth and build a meaningful career within a supportive, forward-thinking company culture.
- Competitive compensation package with significant growth potential as you contribute to GAPVelocity AI’s ambitious expansion goals.
Compensation & Benefits Package:
- Competitive base salary with uncapped commission potential
- $250K-$300K+ total earnings for successful performers
- Comprehensive benefits package including medical, dental, and vision insurance, 401(k) with company matching, and generous PTO policy
- Professional development for conferences, certifications, and skill enhancement
Your Growth Path: Within your first year you’ll master our GAP Velocity AI solutions and build a strong pipeline. By year three, successful individuals often advance to senior roles managing strategic accounts or transition into sales management positions. We believe in promoting from within and provide clear pathways to leadership roles across our growing organization.
Ready to elevate your sales career and help shape the future of AI-driven modernization? We'd love to connect.
The GAP Difference
At GAP, we're proud of our people-centric approach. We provide a place for you to grow and develop your career through engagement with cutting-edge technologies in AI, data engineering, and custom software development. Our open organizational culture values transparency, inclusivity, and adaptability. We operate remotely while building strong relationships through collaboration. Successful GAPsters have a growth mindset and thrive in our dynamic environment.
Career Impact & Growth:
- Autonomy & Impact: Opportunity to shape GAP's client roster and revenue trajectory by targeting high-potential new accounts and introducing cutting-edge AI solutions
- Culture of Innovation: Join a company that values strategic thinking, continuous learning, and empowers employees to make a measurable impact
- Leadership Access: Work closely with executive leadership, including the Chief Revenue & Marketing Officer, to influence go-to-market strategy and client engagement
- Heavy Investment in Your Success: Company-supported relationship marketing, premium events, and F2F lead generation
- Mission-Driven Environment: Be part of a diverse, people-first culture committed to improving lives through technology and community impact
- Bring Your Network: Existing client relationships and book of business welcomed and rewarded
Requirements
Minimum requirements for consideration:
- 10+ years in consultative enterprise sales, selling technology services, modernization, or AI-driven solutions
- Proven track record selling to CIO or technical executive personas (VP Engineering, CTO, Chief Architect)
- Enterprise-grade sales process experience (MEDDIC, BANT, Command of the Message, or similar) with demonstrated ability to manage complex, multi-stakeholder sales cycles
- Basic experience or familiarity with technical debt, platform modernization, or legacy system challenges in enterprise technology sales
- Demonstrated grit and persistence in navigating long sales cycles (6+ months) with multiple stakeholders
- Ability to travel approximately 25% of the time supporting the Northeast, United States
- Legally authorized to work in the United States without company sponsorship, now or in the future
Benefits
- Competitive Healthcare Benefits - Medical, dental, and vision coverage
- Generous Paid Time Off - Flexible vacation policy to support work-life balance
- Remote Work Support - Company-provided laptop and equipment
- Financial Wellness - 401(k) retirement plan, Life insurance (Basic, Voluntary & AD&D), Short-term & Long-term disability coverage
- Family Support - Comprehensive family leave (Maternity, Paternity)
- Professional Development - Training & development opportunities and direct mentorship from senior sales leadership
- Performance Bonuses - Additional earning opportunities beyond base + commission
- Wellness Resources - Supporting your overall well-being
Working at Growth Acceleration Partners:
The foundation of Growth Acceleration Partners (GAP) is an excellent culture, where we align with three values: strive for Greatness, be Agile and invest in People. We foster a culture of belonging and strive to provide a welcoming environment where everyone feels safe to contribute and grow. You will work in a smaller and more intimate environment with a stable and mature company and a team that is super friendly and supportive! All our U.S. employees get a lot of experience with diverse technologies and applications working with our many different types of clients. You will also get opportunities to work with colleagues in Costa Rica, Colombia and across Latin America.
About Growth Acceleration Partners:
At Growth Acceleration Partners (GAP) (WeAreGAP.com), we consult, design, build and deliver revenue-generating software and data solutions for clients. With modernization services and AI tools, we help businesses achieve a competitive advantage through technology. Our comprehensive services include custom software development, data engineering, AI solutions, and strategic technology consulting. GAP expertly builds and manages remote, integrated engineering teams so customers can scale smarter and remain on the cutting edge.
For more than 20 years, we have specialized in designing and building smart digital products for companies of all sizes nationwide. We are a seasoned, innovative team of advisors, architects, engineers, designers and strategists with expertise in user experience design, digital product development and distributed workforce enablement. Our work helps clients generate new revenue streams, maximize profits and gain competitive advantages.
The practical application of technology to enable big ideas is what excites us, and we strive to make a lasting impact on the businesses with whom we partner. We collaborate with our clients to guide their visions, and we’re dedicated to partnering long-term to make lasting digital transformations.
Growth Acceleration Partners is an Equal Opportunity Employer that complies with the laws and regulations set forth in the following *"EEO is the Law" Poster* *and the* *"EEO is the Law" Poster Supplement*. Qualified applicants will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. We encourage and invite all qualified interested applicants to apply for career opportunities. If you are a person with a disability and need a reasonable accommodation to apply for a career opportunity, please send your request to lsehres@growthaccelerationpartners.com and be as specific as possible with respect to your request.
Salary Context
This $250K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $170K across 1414 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Growth Acceleration Partners, 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 $210,000 based on 1,345 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $202,000. This role's midpoint ($275K) sits 31% above the category median. Disclosed range: $250K to $300K.
Across all AI roles, the market median is $220,000. Top-quartile compensation starts at $260,000. The 90th percentile reaches $311,800. For comparison, the highest-paying categories include Research Scientist ($260,000) and AI Architect ($251,680). By seniority level: Entry: $125,000; Mid: $202,000; Senior: $240,000; Director: $255,600; VP: $225,000.
Growth Acceleration Partners AI Hiring
Growth Acceleration Partners has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, MA, US, CT, US. Compensation range: $300K - $300K.
Remote Work Context
Remote AI roles pay a median of $193,725 across 129 positions. About 7% of all AI roles offer remote work.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $220,000. Top-quartile compensation starts at $260,000. The 90th percentile reaches $311,800. Highest-paying categories: Research Scientist ($260,000 median, 48 roles); AI Architect ($251,680 median, 9 roles); Research Engineer ($250,200 median, 8 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $220,000. Top-quartile roles start at $260,000, and the 90th percentile reaches $311,800. 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. Research Scientist roles lead at $260,000 median, while AI/ML Engineer roles sit at $210,000. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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