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About This Role
AboutBilld
Billd is a fast\-growing fintech company looking to disrupt a $1\.5 trillion industry. We offer first\-of\-its\-kind, industry\-leading financial and technology products to empower our customers, commercial subcontractors. We believe in championing the underdog because no one else does.
You will be challenged to bring your best self to Billd and guaranteed to have the most professionally rewarding experiences of your career. We pride our team on being focused, relentless, and driven, but never take ourselves too seriously and love having fun.
As a rapidly growing company, we offer several opportunities for internal growth and career development. We're committed to the motivated professionals that work at Billd, but don't just take our word for it. Check out our 4\.3 Glassdoor rating to see what our team has to say about working here.
But the praise doesn't stop there. Some of our awards include:
- 4x Top Private Companies award winner (Austin Business Journal)
- 4x Fastest Growing Companies award winner (Austin Business Journal)
- 4x Top Technology Firm (Construction Executive Magazine)
- 2x Top Tech Employer in Austin (Austin Business Journal)
- 2x Best Places to Work (Forbes, BuiltIn)
- 1x Best CEO Nomination (Austin Business Journal)
- 1x Top 10 Startups in Austin (LinkedIn)
We call our Austin, TX headquarters home. Our dog\-friendly office is located downtown and features a fully stocked kitchen, onsite fitness room, and hosts quarterly company\-wide events. This role follows an in\-office Monday–Thursday schedule, with Friday remote (if desired). There is added flexibility available through our work from home allowance. This role is not eligible for fully remote work.
About the Role
We're looking for a Growth \& AI Marketing Lead: a builder who uses AI to do what used to take a whole team. This is a new kind of marketing role where you’ll be part systems\-thinker, part storyteller, entirely AI\-native.
Your job is to build and own the engine that produces Billd's marketing at scale. You'll design AI\-powered workflows and automations that research, draft, design, and ship a steady stream of campaigns, sales enablement materials and brand assets, then own the quality bar on everything that comes out of them. Think of it as delegating the repetitive work to systems you build, so your time goes to the human craft a machine can't create.
The role is intentionally hands\-on. In the early days you'll ship work while you’re building to do it for you. We're looking for someone who can design, run, and learn from those systems, not just oversee them. If you see a manual process and immediately want to automate it, this is your kind of role.
You'll also be the voice behind a lot of what Billd says: ghostwriting for leadership, shaping our social presence, and turning big ideas into content people actually stop for, and using video as leverage in customer acquisition. You're persuasive, you read a room, and you can rally people around an idea.
You'll report to our VP of Marketing and work shoulder\-to\-shoulder with our Demand Generation and Marketing Operations Managers. You'll own your work end to end, and we'll trust you to build. This is a hands\-on team where everyone ships, and ownership means doing the work, and being responsible for the results, not just directing it.
What You'll Build \& Own
Build the engine (the core of the job):
- Continuously push the frontier: evaluate new AI tools, build the workflows that keep Billd faster and leaner than teams three times our size, and document the systems so they outlast any single campaign.
- Translate ambiguous business, product, and market needs into structured workflows, reusable prompts, and practical AI\-enabled tools that the whole team benefits from.
- Design, build, and own AI\-powered content and marketing systems: agents, automations, and reusable workflows that generate on\-brand social, email, collateral, and creative on a reliable cadence.
- Build a content repurposing machine: one asset (a webinar, market report, blog, or customer story) becomes social posts, graphics, email, short copy, and sales collateral, largely automatically.
Own the outcomes the engine produces:
- Partner on email, landing pages, SEO, and the website: build, optimize, and keep them current in HubSpot and beyond.
- Turn performance data into action. Know what's working across HubSpot, Google Analytics, and social, kill what isn't, and test relentlessly.
- Bring ideas that open opportunities and support new business, and pitch in wherever marketing can help Billd win.
- Own Billd's organic social presence end to end (strategy, calendar, quality, and community), powered by the systems you build.
- Ghostwrite and produce thought leadership for and with our CEO and leadership team.
- Hold the line on brand quality and creative judgment across everything that ships. You're the taste; the systems are the throughput.
- Capture, produce, and edit video for social, campaigns, and events (increasingly AI\-assisted).
Who You Are
- AI\-native, and you can prove it. You already build with AI, not just prompt it. Automations, agents, custom workflows, reusable prompts, light scripting or no\-code orchestration; you'll show us what you've built and how.
- Fluent with modern AI tools (required). You're hands\-on with AI development, automation, and agentic tools (Claude Code, Codex, Warp, GitHub, etc.), and you pick up new ones fast.
- A builder who'll also do the work. You'd rather architect a system that ships 50 assets than hand\-make 5, but you'll happily hand\-make the first 5 to learn what the system needs to do. You take pride in shipping, not just designing.
- A strong communicator with a keen eye for detail. You produce clean, on\-brand, persuasive work across formats without a designer holding your hand.
- Self\-directed and outcome\-obsessed. You don't wait to be told what to do, and you finish what you start. You understand marketing doesn't stop at MQLs. It runs through the funnel to CAC, LTV, and new\-customer acquisition.
- Comfortable with ambiguity. You can take a rough goal and turn it into shipped work without a detailed spec.
- 4\+ years in digital marketing, content, growth, or a hands\-on marketing role, and a demonstrable body of AI\-built marketing work.
- Experience with HubSpot (or comparable) and analytics tools like Google Analytics.
- Bonus: Financial services, construction, or adjacent industry experience; motion graphics or advanced design / AI\-creative tooling; event production.
Why Billd?
Billd is committed to diversity and is proud to be an equal\-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
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 BillD, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
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.
BillD AI Hiring
BillD has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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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