About this role
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.
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