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About This Role
About Climb
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Climb is a Data and AI consultancy that partners with enterprises to design, build, and operationalize modern data platforms and production AI systems. As a Databricks partner, we go deep on lakehouse architecture, machine learning, and applied AI, with a bias toward production over proof of concept. Our team brings deep technical expertise and a builder's mindset to every engagement, and we measure our work not just by what ships, but by the business impact it drives.
Role Summary
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The Forward Deployed AI Lead owns the success of a client engagement from discovery through executive handoff. You align business objectives, technical strategy, delivery execution, and client relationships into a single plan that delivers measurable outcomes. While the Forward Deployed Architect owns the system architecture, you own the engagement itself, ensuring the right problems are solved, the right team is focused on them, and the customer achieves the outcomes they invested in.
This is an individual contributor leadership role. You direct the work of Architects and Forward Deployed Engineers, lead executive conversations, shape engagement strategy, and make the technical and commercial decisions that keep delivery aligned with business value. You remain hands\-on enough to guide critical technical decisions, but your primary responsibility is creating the conditions for the team and the customer to succeed.
You become Climb's trusted advisor throughout the engagement, building executive relationships, identifying future opportunities, and ensuring every engagement leaves the customer better positioned to continue their AI transformation.
Key Responsibilities
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- Own the success of AI transformation engagements from discovery through executive handoff, aligning customer outcomes, technical direction, delivery execution, scope, and long\-term partnership into a single engagement plan.
- Run structured discovery across engineering, product, and business stakeholders to map context, AI readiness, and where AI changes the economics.
- Design target architectures and AI operating models that define how the organization adopts, governs, and scales AI in production.
- Define the enterprise AI strategy and operating model that guide architectural decisions, delivery priorities, governance, and long\-term AI adoption.
- Set strategic and technical direction across architecture, data readiness, AI adoption, governance, and delivery priorities.
- Provide hands\-on technical leadership when critical architectural or delivery challenges require it.
- Build executive relationships that position Climb as a long\-term AI partner, not a one\-time vendor.
- Shape follow\-on scope throughout delivery; by handoff, you have a credible hypothesis for the next engagement and can present it.
- Surface expansion signals across lines of business and route them to the account team.
- Improve delivery frameworks based on engagement experience.
- Mentor Architects and FDEs on architecture, executive engagement, and the architect\-engineer delivery model.
Required Qualifications
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- 10\+ years of software development or technical architecture experience across varied stacks, including cloud environments.
- 3\+ years in engineering leadership, technical consulting, solutions architecture, or a combination.
- Hands\-on familiarity with modern AI engineering workflows, development and agentic tooling (e.g., Claude Code or equivalent), enough to set technical direction and direct an engineer's work credibly.
- Experience designing or advising on AI operating models, technology adoption strategies, or transformation programs.
- Demonstrated ability to engage across lines of business: you can talk CI/CD with a VP of Engineering and operational efficiency with a COO in the same week.
- Strong executive communication: writing and presenting to CTOs, VPs of Engineering, and line\-of\-business leaders.
- Demonstrated ownership of enterprise consulting engagements, including executive stakeholders, scope, delivery quality, and commercial outcomes.
Preferred Qualifications
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- Hands\-on experience with Databricks (Mosaic AI, Unity Catalog, model serving) is a strong plus.
- Direct experience building or operating agentic AI systems in production.
- LLM prompt engineering, RAG architecture, or AI product development experience.
- Familiarity with PE portfolio\-company environments and board\-level communication.
- Experience designing AI governance and adoption guardrails for enterprise organizations.
*Note on certification: Databricks experience is valued but not required at hire. Both Databricks and Anthropic certification are expected to be obtained post\-hire.*
Who Thrives Here
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- You naturally take ownership of entire engagements, bringing technical teams, executive stakeholders, and business objectives together around a common outcome.
- You earn trust with executives because you connect technical decisions to measurable business value.
- You know when to dive into architecture, when to coach the team, and when to challenge the customer's assumptions.
- You recognize delivery, organizational, and commercial risks early and address them before they become problems.
Why Climb
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- Ground floor, real backing. You are joining early, with founders who have built and exited firms like this before. You help write the playbook rather than inherit one.
- Outcomes, not hours. We sell and deliver against business results. Advancement is tied to delivery performance and account impact, not utilization targets.
- Senior team, no body\-shop drag. Small pods of A\-players, heavy internal AI leverage, and no bloated middle layers between you and the work.
- IP that compounds. Every engagement feeds reusable accelerators, patterns, and points of view back into the practice.
- Define the methodology. You shape how AI transformation gets scoped and delivered at Climb. Every engagement you run improves the next.
What We Offer
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- Competitive base salary with performance\-based bonuses
- MacBook Pro and swag kit so you can do your best work
- Comprehensive health, dental, and vision insurance
- Generous holidays, flexible PTO, and remote\-first work environment
- Professional development budget including Databricks and cloud certifications
- Spot bonuses for relevant certifications
- Conference attendance and thought leadership opportunities
- Collaborative, low\-ego culture with direct access to leadership
- Opportunity to shape a growing practice from the ground floor
Climb is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Climb, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Climb AI Hiring
Climb has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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