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About This Role
Business Development Executive (BDE) — AI Infrastructure / GPU Brokerage (W2, Bay Area)
Location: San Francisco Bay Area (hybrid/in-person for key meetings)
Employment Type: Full-time (W2)
Work Authorization: US Citizens or Green Card holders only
Start: ASAP
About Us
We’re building a new line of business focused on GPU compute brokerage + managed operations for AI teams. Our customers are Bay Area AI startups and engineering orgs who need reliable GPU capacity, predictable costs, and smooth operations—without wasting cycles on vendor churn or infrastructure firefighting.
This is a high-trust, technical business development role. We are not looking for lead gen or appointment setting. We need someone who can open doors, run deals, and close.
The Role (What You’ll Do)
You will own new revenue and partnerships.
New Business (Core)
- Prospect and close Bay Area customers (CTO, VP Engineering, Head of Platform/Infra).
- Run discovery calls, qualify fast, and drive opportunities to signed SOWs.
- Maintain clean pipeline tracking and weekly reporting (simple, consistent metrics).
Partnerships / Channels
- Build relationships with GPU cloud providers (partner/referral relationships to start).
- Develop referral channels with VC platform teams, accelerators, and technical partners.
Offer Packaging (With Leadership + Operators)
- Sell a productized set of services: Benchmark Sprint → Production Setup → Managed Ops.
- Provide market feedback to refine messaging, packaging, and objection handling.
What Success Looks Like (First 90 Days)
- Weeks 1–2: Target list built, outbound running, first meetings booked
- By Day 45: 2 paid benchmark sprints closed
- By Day 90: 1–2 retainers in-flight or closed + repeatable pipeline motion established
Who You Are
Must-have
- 5+ years in B2B sales / biz dev selling to technical buyers.
- Proven ability to create pipeline (not just work inbound).
- Strong discovery, negotiation, and close skills.
- Comfortable in early-stage environments (scrappy, fast-moving, ambiguous).
- Excellent follow-through and CRM hygiene.
Nice-to-have (strong plus)
- Sold cloud/infra services, DevOps/SRE, security, FinOps, data infrastructure, or AI infrastructure.
- Existing Bay Area network among startups, CTOs, platform leaders, VCs, or accelerators.
- Experience building partnerships/channels in addition to direct sales.
Compensation & Benefits
Competitive base salary + commission/bonus + standard benefits (details shared during process).
Pay: $140,000.00 - $180,000.00 per year
Benefits:
- 401(k)
- Dental insurance
- Health insurance
- Paid time off
- Vision insurance
Work Location: Hybrid remote in Fremont, CA 94539
Salary Context
This $140K-$180K range is above the median for AI Product Manager roles in our dataset (median: $160K across 8 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 37,339 AI roles we're tracking, AI Product Manager positions make up 2% of the market. At Star Trade Enterprises, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $205,900 based on 289 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000. This role's midpoint ($160K) sits 22% below the category median. Disclosed range: $140K to $180K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Star Trade Enterprises AI Hiring
Star Trade Enterprises has 1 open AI role right now. They're hiring across AI Product Manager. Based in Fremont, CA, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $145,600. 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 (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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.
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