# Jobgether — Director, Applied AI

- Generated: 2026-09-18 07:14:59 AM EDT
- Original/canonical posting: https://www.linkedin.com/jobs/view/4467844532/
- Provider: LinkedIn
- Posted: approximately 2026-09-18 07:04 AM EDT (from '10 minutes ago')
- Elapsed since posting: represented by the provider's relative posting label and approximate Eastern timestamp when available
- Applicants: Be among the first 25 applicants
- Work model/location: Remote — United States; end employer is unidentified
- Compensation: $233,100-$366,300 base plus possible bonus, commission, equity, and benefits
- Travel: Not disclosed
- Positioning track: Technical manager
- Fit outcome: BORDERLINE — 78%

## Direct-match strengths

Hands-on production AI, ML and knowledge systems, agentic coding, evaluation, RAG, graphs, SQL/data platforms, model routing, inference cost controls, hiring, executive communication, and founder product delivery.

## Hard or material gaps

Material central-mandate gaps remain in leakage-safe validation, calibration, propensity modeling, large-scale entity resolution, post-training, distillation, and open-weight model serving. The end employer is unidentified. This appears to be the same underlying role previously posted by ZoomInfo.

## Evidence map

1. Ship production ML and LLM systems hands-on (weight 3, evidence 3/3) — Long ML history plus current production agents and RAG.
2. Hire and develop senior ML/data-science talent (weight 3, evidence 3/3) — Built technical teams and global specialist communities.
3. Classical ML, statistics, experiments, and SQL (weight 3, evidence 2/3) — Direct ML, forecasting, optimization, experiments, and SQL; calibration depth is less explicit.
4. Evaluation datasets and regression gates (weight 3, evidence 3/3) — Direct AI evaluation, synthetic data, LLM-as-judge, regression, and guardrails.
5. Inference cost, latency, capacity, and build-versus-buy (weight 3, evidence 3/3) — Direct model routing, token-cost controls, performance, architecture, and vendor decisions.
6. Data graphs, retrieval, entity resolution, and ranking (weight 3, evidence 2/3) — Knowledge graphs and retrieval are direct; web-scale entity resolution and propensity/ranking are not explicit.
7. Post-training, distillation, and open-weight serving (weight 2, evidence 0/3) — Not source-supported.
8. Hands-on founder/product delivery (weight 2, evidence 3/3) — Direct 0-to-1 founder and product evidence.

## Keyword diagnostic

Strong production AI, agents, evaluation, graph/retrieval, cost, leadership, and founder alignment; specialized modern data-science and model-serving depth remains incomplete.

## Full normalized job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Director, Applied AI based in United States.
This role leads the team responsible for the intelligence that powers AI agents and decision-making systems across a B2B data environment.
You will own the data graph strategy end to end, spanning training data, machine learning, LLMs, agentic systems, and model serving.
The position combines technical leadership with hands-on engineering, prototyping, experimentation, and production delivery.
You will guide a small, senior team while setting a high technical bar for model quality, evaluation, scalability, and reliability.
The role offers broad cross-functional exposure, partnering with product, platform, security, legal, and executive stakeholders.
You will also shape how AI development tools are used across the team, balancing speed of execution with rigorous engineering standards.
This is an opportunity to influence foundational AI capabilities while developing senior technical talent and delivering measurable production impact.
Accountabilities
Lead the strategy and development of a comprehensive B2B data graph, extending coverage to organizations with limited public information.
Own the end-to-end data graph lifecycle, from training data and modeling through production model serving.
Develop systems that enable AI agents to reason over accurate information about companies, people, relationships, and purchasing activity.
Lead initiatives around agent memory, clearly distinguishing user-provided information from data already maintained by authoritative systems.
Select the most appropriate technical approach for each problem, including classical machine learning, language models, agentic systems, and code-based solutions.
Use measured evidence to prioritize high-value initiatives and discontinue approaches that are unlikely to deliver sufficient impact.
Establish evaluation standards for machine learning models and AI agents, ensuring quality claims are measurable and trustworthy.
Build evaluation datasets, regression gates, experiment frameworks, and validation processes for continuous model improvement.
Own inference cost, latency, and capacity alongside model quality and system performance.
Make informed build-versus-buy decisions and assess opportunities for model distillation and optimization.
Hire, mentor, and develop machine learning engineers, data scientists, and research engineers.
Help senior engineers grow into technical leadership roles while maintaining a strong engineering culture.
Remain hands-on by writing production code, developing prototypes, and working directly alongside the engineering team.
Establish standards for the effective use of agentic coding tools, combining precise specifications with rigorous code review.
Collaborate with product, platform, security, legal, and other stakeholders to align technical work with broader business and operational requirements.
Communicate technical results, limitations, trade-offs, and risks clearly to executive stakeholders.
Set and maintain a high technical bar for production machine learning, data science, AI agents, and supporting infrastructure.
Requirements
Significant demonstrated experience building and shipping production machine learning systems, with hands-on technical leadership.
Proven experience hiring, mentoring, and developing senior machine learning engineers and data scientists.
Current hands-on engineering experience, including writing code, building prototypes, and contributing directly to production systems.
Strong expertise in classical machine learning and data science, including supervised learning, feature engineering, statistical inference, and experiment design.
Strong SQL skills and the ability to work effectively with complex datasets and data-intensive systems.
Production experience with LLMs and agentic AI systems, including the judgment to determine when these approaches are appropriate.
Experience establishing rigorous model evaluation standards, including leakage-safe validation and calibration.
Demonstrated ownership of inference cost, latency, capacity, and operational considerations alongside model quality.
Experience making build-versus-buy decisions for machine learning and AI capabilities.
Strong ability to communicate technical findings, limitations, and trade-offs to executive audiences.
Entrepreneurial experience is preferred, such as founding a company or taking a product from inception to paying customers as a founding or early engineer.
Experience with propensity modeling, ranking and retrieval, clustering, or large-scale entity resolution is preferred.
Experience with web-scale language processing involving multilingual or noisy text is a plus.
Experience working with knowledge graphs or agent memory systems is preferred.
Experience with model post-training and distillation is advantageous.
Familiarity with open-weight model serving is a plus.
Knowledge of AI governance and safety practices, including ISO/IEC 42001 or the NIST AI Risk Management Framework, is preferred.
Benefits
Base salary range of $233,100–$366,300 USD for the United States.
Additional compensation may include bonus, commission, equity, and other benefits, depending on the position and applicable factors.
Comprehensive benefits designed to support employees and their families.
Holistic mind, body, and lifestyle programs focused on overall well-being.
Compensation may vary based on work location, qualifications, skills, experience, and training.
Remote work arrangement.
How Jobgether Works
We use an
AI-powered matching process
to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether?
Data Privacy Notice:
By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

## Artifact metadata

- Resume: https://bit.ly/3TF2Vmy
- Cover letter: https://bit.ly/4ivnMD0
- Validation: PASS — 2-page resume (900 words), 1-page cover letter (196 words); PDF geometry, bounds, annotations, links, and visual pages verified.
- Google Drive used: No