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About This Role
Director of Retail Sales \- Semsee MAP — Wholesale Brokerage
Location: Remote \- U.S.
Reports To: Head of GTM
Direct Reports: 3\-7 Sales Reps
Type: Full\-Time
About Semsee
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Semsee is building the only independent digital platform that covers the full small commercial insurance lifecycle — quoting, binding, and policy administration — across both admitted and E\&S lines. Our Managed Access Program (MAP) is a technology\-powered wholesale brokerage that gives retail agents instant access to 50\+ carrier markets through a single platform. Agents enter risk information once and get comparative quotes in minutes, not days.
MAP is our primary growth engine, and we're investing aggressively in building a dedicated sales organization for the first time. Backed by Adir Ventures, we have the capital, the technology, and the carrier relationships. What we're adding now is the go\-to\-market muscle.
About the Role
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We're looking for a Director of Retail Sales to own premium growth across an assigned region by building, leading, and optimizing a team of 3–7 Regional Sales Managers. This is a player\-coach role at a company that is standing up its first professional sales organization — you'll carry strategic relationships of your own while spending the majority of your time making your team better.
You are the person who turns a hiring plan into a functioning sales team. You will recruit, onboard, train, and coach reps from day one. You will set territory plans, build pipeline discipline, run the weekly operating rhythm, and hold your team accountable to premium targets — while ensuring they have the product knowledge, carrier appetite intelligence, and platform fluency to win.
This role reports to the Head GTM. The right candidate has managed wholesale or MGA sales teams, knows how retail agents make placement decisions, and has a track record of building teams that hit numbers — not just inheriting teams that already do.
What You'll Do
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- Build the Team: Recruit, hire, and onboard 3–7 Regional Sales Managers in your region. Define role expectations, ramp plans, and performance benchmarks. You own the quality of every hire and the speed at which they become productive.
- Train and Develop: Stand up a repeatable onboarding and training program covering MAP's product suite, carrier appetite, platform workflows, the competitive landscape, and consultative selling to retail agents. Continuously coach reps through deal reviews, ride\-alongs, and pipeline sessions.
- Drive Regional Premium Targets: Own the regional written premium number. Translate company\-level targets into territory plans, agency\-level goals, and weekly activity metrics. Know where every dollar of pipeline sits and what it takes to convert it.
- Operate the Sales Rhythm: Run weekly pipeline reviews, monthly business reviews, and quarterly territory planning sessions. Build the forecasting discipline and reporting cadence that lets leadership see around corners. Ensure your team updates CRM religiously and pipeline data is trustworthy.
- Optimize Execution Day\-to\-Day: Diagnose rep\-level performance gaps and fix them — whether the issue is activity volume, agent targeting, platform adoption conversations, or close technique. Remove obstacles. Accelerate wins. Intervene early on underperformance.
- Carry Strategic Relationships: Maintain a personal book of high\-value agency relationships in the region. Model the consultative selling approach you expect from your team. Join reps on key calls and use those moments as live coaching opportunities.
- Collaborate Cross\-Functionally: Partner with underwriting, operations, product, and marketing to ensure your team has the tools, carrier access, and content they need. Relay regional market intelligence that shapes product roadmap and carrier development priorities.
- Shape the Sales Playbook: As a founding sales leader, you will directly influence hiring profiles, compensation structures, sales methodology, territory design, and the tools and processes that scale beyond your region. What works in your region becomes the template for the company.
What We Look For
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- 5\+ years in commercial P\&C insurance sales, with at least 2 years in a people\-management role leading a team of 3 or more sales professionals
- Direct experience in wholesale, E\&S, MGA, or binding authority environments — you understand the wholesale value chain and how retail agents choose where to place business
- Demonstrated track record of building or significantly growing a sales team, not just managing an inherited one — you've recruited, onboarded, trained, and coached reps to quota
- Ability to operate as a player\-coach: credible in front of agency principals on your own, and effective at making your team better through structured coaching and deal support
- Fluency in pipeline management, forecasting, and CRM discipline — you run a data\-driven sales operation, not a relationship\-driven one
- Comfort operating in an early\-stage environment where you're building process, not following it — your initiative and judgment matter more than your title
- Strong knowledge of small commercial lines (GL, BOP, WC, commercial property, commercial auto) and the agent workflow for placing these risks
- Experience with CRM tools (HubSpot, Salesforce) and comfort working in a technology\-forward, platform\-driven sales environment
- Willingness to travel within your region as needed to support team development, key agency meetings, and market presence
What Makes This Different
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- You're building the sales org, not joining one. This is a founding leadership role. The playbook, the team, the culture, the operating rhythm — you're creating it. What you build here becomes the model for every region that follows.
- Technology is the product, not just a tool. Your team is selling a platform that gives agents access to 50\+ admitted and E\&S markets instantly, with AI\-powered underwriting automation that processes routine bind orders without human intervention. The technology closes deals — your job is to build a team that gets agents onto it.
- Breadth across admitted and E\&S. Unlike competitors focused exclusively on E\&S, MAP gives agents access to both admitted and surplus lines markets — a broader solution that covers more of their book and gives your team a wider aperture for every conversation.
- Ground floor with real backing. We're backed by committed growth capital from Adir Ventures. You're joining early enough to shape the entire go\-to\-market function, but with enough runway and infrastructure that this is a plan, not a prayer.
- A stellar quote\-to\-bind ratio. The platform works. Agents who use it bind business at nearly three times the industry rate. Your team's job is distribution and relationship — the technology handles conversion.
What We Offer
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- Growth Opportunity: Ground floor opportunity as an early member of the Semsee team
- Our Culture: An inclusive company culture that is being built intentionally to foster stimulating, healthy, meaningful and long lasting work relationships.
- Fully Remote Team Environment
- Time Off: 4 weeks (20 Days) \+ 9 company observed holidays; Total 29 Days/Year
- Benefits: 70% Funded Medical/Vision/Dental; 100% Funded Life Insurance; 401k, Roth IRA; Generous 12 Weeks Paid Baby Bonding Leave, \+more
Compensation
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Base salary of $90,000–$110,000 plus performance\-based variable compensation tied to regional premium production and team development targets. Target total compensation of $200,000–$275,000 for on\-target performance.
*Semsee is an equal opportunity employer. We consider all qualified applicants without regard to race, color, religion, sex, national origin, disability, veteran status, sexual orientation, gender identity, or any other legally protected status.*
Salary Context
This $90K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 Semsee, 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 $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288. This role's midpoint ($182K) sits 9% above the category median. Disclosed range: $90K to $275K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Semsee AI Hiring
Semsee has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $275K - $275K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 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 $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $122,200. 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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