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### About Fulcrum
The $1\.5 trillion insurance industry is powered by brokers. Every business needs insurance, and the brokerage industry is hamstrung by account management capacity. Account managers are constantly pulled in multiple directions—keying information into systems, chasing documents, doing mundane work—rather than focusing on the client interactions that actually matter. Most brokerages either outsource this work or grind through it manually.
We're building an AI platform to automate this insurance drudgery. Our platform already saves customers thousands of hours and millions of dollars every year. Our customers love the product— we have deep penetration within enterprise brokerages and genuinely enjoy making our customers' lives better. We just raised our Series A and are just getting started.
Why Fulcrum
- Rocket\-ship growth. We’ve achieved multiple seven figures in ARR in just over a year, serving large enterprise clients including 30% of the top 50 brokers in the country.
- Extreme ownership. You’ll work directly with enterprise users and ship end\-to\-end products that save them hours every week.
- Bleeding\-edge problems, real impact. Tackle tough AI and product challenges whose wins show up immediately in customer workflows.
- In\-person collaboration. Join a lean, staff\-level team (ex\-Affirm, Uber, DoorDash, McKinsey) working side\-by\-side in San Francisco; we believe the best ideas are fostered in an in\-person environment.
- Top\-of\-market rewards. Competitive base salary plus meaningful founding\-team equity.
### About the Role
As an AI Deployment Strategist at Fulcrum, you will work directly with our largest customers — the top 100 insurance brokerages — to guide them through AI transformation. You’ll be responsible for ensuring the successful deployment, adoption, and integration of Fulcrum into core brokerage systems and workflows.
This is a customer\-facing and product\-facing role. You will serve as a trusted advisor to brokerage executives and teams, while also working hand\-in\-hand with Fulcrum’s forward\-deployed engineers and product managers to translate broker needs into solutions. Your success will be measured by how effectively you drive adoption, deliver measurable business value, and expand Fulcrum’s footprint across enterprise accounts.
### What You’ll Do
- Trusted Advisor: Serve as the primary point of contact for enterprise brokerage partners, guiding them through rollout, adoption, and integration of Fulcrum.
- AI Transformation Leadership: Partner with executives and operations leaders to shape AI strategies that modernize how brokers market, service, and grow accounts.
- Customer Implementations: Manage the full deployment lifecycle for enterprise accounts — from scoping custom requirements and designing rollout plans to coordinating technical integration with internal systems (AMS, CRM, document repositories).
- Product Translation: Synthesize broker needs into product requirements; work closely with forward\-deployed engineers to design and implement solutions.
- Partner with Forward\-Deployed Engineers: Collaborate daily with engineering teams to configure Fulcrum for unique customer environments — whether that's custom data integrations, workflow automations, or specialized AI models.
- Strategic Business Reviews: Lead executive\-level reviews, quantifying ROI and driving account expansion.
- Cross\-Functional Collaboration: Bridge the gap between customer stakeholders, Sales, Product, and Engineering, ensuring smooth implementations and rapid iteration.
- Training \& Adoption: Enable CSRs, producers, and executives to get the most out of Fulcrum’s platform, ensuring sustained adoption and impact.
### Who You Are
- 4–8 years of experience in one or more of: management consulting, technical account management, solutions engineering, insurance brokerage operations, or AI/SaaS deployment roles.
- Technical fluency: Comfortable working alongside engineers to scope integrations, troubleshoot technical issues, and understand system architecture. You don't need to code, but you can speak the language.
- Customer implementation expertise: Proven track record managing complex, multi\-stakeholder rollouts — coordinating across customer teams, internal engineering, and product to deliver on time and drive adoption.
- Strong stakeholder management: Able to build trust and influence with senior executives, while also rolling up your sleeves with frontline teams to understand day\-to\-day pain points.
- Analytical and impact\-driven: Skilled at defining success metrics, tracking progress, and translating technical capabilities into business outcomes.
- Bias toward action in ambiguity: Thrives in fast\-moving environments where requirements aren't always clear and solutions need to be invented, not just implemented.
- Excellent communicator: Can translate complex technical concepts for business audiences and articulate business needs for engineering teams. Comfortable leading executive presentations and workshops.
### Preferred Experience
- Background in insurance brokerage (operations, client service, technology) or insurtech.
- Exposure to AI/ML product deployments or automation platforms.
- Experience with enterprise integrations (APIs, data pipelines, AMS/CRM systems).
- Prior work in a deployment strategist, solutions consultant, or forward\-deployed role at a technical company.
### What we offer
- Competitive salary.
- Full health, dental \& vision.
- High\-ownership, high\-trust culture with lightning\-fast executors.
- Regular team off\-sites, dinners, and an office stocked for builders.
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 Fulcrum, 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.
Fulcrum AI Hiring
Fulcrum has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% 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 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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