Principal AI/ML Engineer - USA

Posted 3 Hours Ago
Be an Early Applicant
8 Locations
In-Office or Remote
136K-213K Annually
Expert/Leader
Artificial Intelligence • Information Technology • Machine Learning • Consulting
The Role
Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.
Summary Generated by Built In

About the Role

We are seeking a Principal AI/ML Engineer to serve as a technical leader and architect for our organization’s AI/ML strategy, systems, and platforms. In this role, you will define the long-term technical vision for machine learning and MLOps, drive cross-team alignment on architecture and standards, solve the hardest technical problems, and ensure our ML capabilities are enterprise-grade, scalable, and forward-looking. The ideal candidate is a recognized technical authority who combines deep hands-on expertise with strategic thinking, organizational influence, and the ability to elevate the entire engineering team.

Key Responsibilities

  • Define and own the long-term technical vision, architecture, and roadmap for the organization’s AI/ML platform and MLOps capabilities.

  • Serve as the senior technical authority on ML system design, providing guidance on architecture, tooling, and engineering standards across the organization.

  • Lead the design of foundational ML infrastructure including training platforms, feature stores, model registries, serving systems, and observability frameworks.

  • Establish and evangelize best practices for the full ML lifecycle: experimentation, development, testing, deployment, monitoring, governance, and retirement.

  • Drive cross-functional alignment between ML engineering, data engineering, platform engineering, product, and security teams.

  • Evaluate emerging technologies, frameworks, and research to inform strategic decisions on AI/ML tooling and approaches.

  • Solve complex, ambiguous, and high-impact technical problems that span multiple teams or systems.

  • Mentor and develop senior and staff-level engineers, fostering a culture of technical excellence and continuous improvement.

  • Represent the engineering organization in discussions with leadership, partners, and clients on AI/ML capabilities and strategy.

  • Define and enforce governance, compliance, and responsible AI practices across ML systems.

  • Drive cost optimization and efficiency across ML training, serving, and infrastructure.

  • Contribute to hiring, technical assessments, and the growth of the AI/ML engineering team.

Required Qualifications

  • Master’s or PhD in Computer Science, Mathematics, Statistics, or a related field preferred. Equivalent professional experience is accepted.

  • 10+ years of professional experience in software engineering, ML engineering, or applied AI, with at least 5 years focused on production ML systems.

  • Deep expertise in designing and scaling production ML platforms, pipelines, and infrastructure.

  • Authoritative knowledge of MLOps principles, tools, and practices including CI/CD for ML, automated retraining, model governance, and observability.

  • Extensive experience with cloud-native ML services across AWS, Azure, or GCP, and infrastructure-as-code tools (Terraform, Pulumi, CloudFormation).

  • Strong understanding of distributed systems, data architecture, and scalable platform design.

  • Proven track record of driving technical strategy and influencing engineering direction at an organizational level.

  • Experience leading and mentoring senior engineers and building high-performing technical teams.

  • Exceptional communication skills with the ability to align technical and business stakeholders on complex topics.

  • Strong understanding of responsible AI, model fairness, explainability, security, and compliance requirements.

Preferred Qualifications

  • Experience architecting LLM-based systems, RAG pipelines, agentic AI platforms, or conversational AI at enterprise scale.

  • Experience with MCP/tool-layer integrations for LLM-driven systems.

  • Deep familiarity with feature platforms, model serving at scale, and real-time inference architectures.

  • Track record of contributing to or leading industry standards, publications, or open-source projects in AI/ML.

  • Experience with enterprise AI governance frameworks and regulatory compliance (SOC2, HIPAA, GDPR).

  • Experience working across geographically distributed or offshore engineering teams.

  • Background in financial services, healthcare, or other highly regulated industries.

Salary Range
US East/West Coast: $159,800 - $213,100
US Remote: $135,900 - $181,200
Disclaimer: These salary ranges are estimates based on market data and may vary depending on factors such as experience, skills, and specific job requirements. Final compensation is subject to individual qualifications and company policy.

Perks And Benefits Of Working With Us

  • Unlimited PTO.

  • Please ask us about our very generous parental leave, much above industry standards!.

  • Entrepreneurial culture where pushing limits and taking risks is everyday business.

  • Open communication with management and company leadership.

  • Small, dynamic teams = massive impact.

  • Medical, Dental and Vision coverage for employees.

  • Access to Disability & Life insurance.

  • Mental health and wellbeing support

  • Annual bonus program

  • Employer Stock Purchase Program (ESPP)

  • Yearly Team building experiences

  • Mentorship and sponsorship opportunities

  • Manager resources and support

We are 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, veteran status, or any other protected characteristic.

Skills Required

  • Master's or PhD in Computer Science, Mathematics, Statistics, or related field (or equivalent experience)
  • 10+ years professional experience in software engineering, ML engineering, or applied AI
  • At least 5 years focused on production ML systems
  • Deep expertise designing and scaling production ML platforms, pipelines, and infrastructure
  • Authoritative knowledge of MLOps principles, CI/CD for ML, automated retraining, model governance, and observability
  • Extensive experience with cloud-native ML services on AWS, Azure, or GCP
  • Experience with infrastructure-as-code tools (Terraform, Pulumi, CloudFormation)
  • Strong understanding of distributed systems, data architecture, and scalable platform design
  • Proven track record of driving technical strategy and influencing engineering direction at organizational level
  • Experience leading and mentoring senior engineers and building high-performing technical teams
  • Exceptional communication skills to align technical and business stakeholders
  • Strong understanding of responsible AI, model fairness, explainability, security, and compliance requirements
  • Experience architecting LLM-based systems, RAG pipelines, agentic or conversational AI at enterprise scale
  • Familiarity with feature platforms, large-scale model serving, and real-time inference architectures
  • Experience with enterprise AI governance frameworks and regulatory compliance (SOC2, HIPAA, GDPR)
  • Experience working across geographically distributed or offshore engineering teams
  • Background in financial services, healthcare, or other highly regulated industries
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The Company
0 Employees
Year Founded: 2025

What We Do

Cogniify is a Bay Area-based AI execution firm that designs, builds, and deploys custom AI systems for Fortune 500 and Global 2000 companies. The company helps enterprises move from AI pilots to industrialized impact and enterprise-scale production, utilizing deep expertise in AI, advanced analytics, data engineering, and domain consulting.

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