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About This Role
Who We Are
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Product: TLDR exists to increase tech's signal\-to\-noise ratio. Where we're going: to be tech's intellectual Schelling point \- where the future starts its day.
Today that means the largest network of tech newsletters in the world, with over 8M subscribers covering startups, software engineering, AI, cybersecurity, product, and more. What makes it work is who writes it. Every issue comes from people building in tech, not reporters covering it. Our writers keep their day jobs: two engineers at Coinbase write TLDR Crypto, engineers at DeepMind and Meta write TLDR Dev, researchers at Anthropic and Adobe write TLDR AI, and robotics and datacenter strategy leads at OpenAI and Meta write TLDR Hardware.
If it matters in tech, it's in TLDR. That's what makes TLDR the best place to find what you need to learn.
Team: Our 29\-person full\-time team includes alumni of TikTok, Reddit, Amazon, Business Insider, Asana, Morning Brew, and Pinterest. We've stayed intentionally small, which means every person here owns a function rather than a slice of one.
Traction: We're bootstrapped, profitable, and on track for $35M in revenue this year \- after $9M in 2024 and $20M in 2025\. The advertisers who fund it want tech's decision\-makers: AWS, Google Cloud, Anthropic, Slack, Notion, and GitHub.
About the Role
==================
The Go\-to\-Market Engineer owns TLDR's GTM systems end\-to\-end: the input and interface layer where our team interacts with the revenue stack. This is a building role, not just maintenance \- you'll decide what's worth quick\-fixing, automating, or re\-architecting, and bring an AI\-first lens to problems RevOps teams have historically solved by hand. The role sits inside Applied AI, giving you real autonomy over a revenue\-critical domain and a seat close to how TLDR builds AI into its GTM systems, work that directly supports our \~$35M FY26 revenue target.
In this role, you will:
- Own GTM systems end\-to\-end: HubSpot administration, attribution, quota updates, new\-product launches, quarter\-over\-quarter transitions, and CSAT plumbing.
- Own data integrity: build pipelines right and keep fields accurate and flowing downstream.
- Diagnose recurring problems, decide whether to quick\-fix, automate, or re\-architect, and build the fix.
- Evaluate net\-new systems (e.g., Sponsy alternatives) and recommend a path forward.
- Bring an AI\-first lens to classic RevOps problems like attribution, and partner with the Head of Applied AI on the broader AI × GTM operating model.
What Success Looks Like
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*In your first 6 months:*
- You're the single accountable owner of the GTM systems ring, with the contractor\-held work transitioned to you.
- You've built the data\-integrity layer \- dashboards on field completeness and pipeline health, with alerting on top so no one checks by hand.
- You've shipped your first wave of automations and agent workflows, each with monitoring attached.
- You've documented it \- fix\-it procedures good enough that a break doesn't require you.
About You
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- Main background: 4–6 years owning GTM systems at a B2B SaaS or B2B tech company \- ideally from an early\-stage (Series B–C) environment where you ran GTM systems end\-to\-end as a single owner
- Primary skills: HubSpot ownership beyond admin; strong critical thinking and consultant\-style communication; a product mindset for solution design balanced with pragmatic urgency; root\-cause diagnosis paired with quick, clear proposals; hands\-on data\-integrity ownership (pipeline monitoring, field population, accuracy).
- Secondary skills: Fluency in what AI\-first GTM systems look like, applying AI to problems like attribution; familiarity with how APIs and integrations work.
- Nice\-to\-haves: SQL
You’ll Thrive Here If You…
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- Want full ownership of a revenue\-critical domain, with real autonomy and accountability, not just a task list.
- Are comfortable operating without a paved playbook, this role has more ambiguity than structure.
- Want to sit inside an Applied AI/engineering\-focused team rather than a traditional RevOps team, and help define what GTM systems look like in an AI\-native org.
Why You'll Love It
======================
Compensation: $175,000 \- $205,000 \+ annual company performance bonus
Location: We're 100% remote across the US and Canada. Work where you want \- our bands are set to tier\-1 city rates wherever you live.
Team Events: Biannual team offsites. Most recently we’ve gone to New Orleans, San Diego and Park City!
Time to Recharge: Flexible PTO. Most of the team takes 2–3 weeks a year, plus holidays.
Health Benefits: Comprehensive medical, dental and vision benefits with a 100% paid option
401(k) Plan: Empower 401(k)
Paid Parental Leave
Home Office Stipend: Whatever makes you productive \- standing desk, second monitor, chair, walking pad.
Learning \& Development Stipend: For anything that makes you better at your work, and for AI tools especially. Don't ration your tokens.
If you're ready to make a tremendous impact at a bootstrapped and profitable startup, please apply. Please let us know if you need any accommodation during any part of the interview process.
Press and Publications
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- Inc story on how TLDR was founded
- Pricing and demographic information in TLDR’s latest media kit
Compensation Range: $175K \- $205K
Salary Context
This $175K-$205K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At tldr, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($190K) sits 12% below the category median. Disclosed range: $175K to $205K.
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.
tldr AI Hiring
tldr has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $205K - $205K.
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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