Principal AI Engineer

Heads up: this posting is for future opportunities rather than one specific open role. If you apply, we'll add you to our candidate network and may reach out when relevant roles come up.

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What the market looks like

We've tracked 34,500+ mid-level AI/ML engineering postings across the US in the last six months, concentrated in technology, financial services, and professional services hubs like San Francisco, New York, and the broader California corridor. Compensation for this cohort typically ranges from $160k to $350k, with a median midpoint around $180k. The strongest candidates combine deep experience shipping production AI systems with hands-on fluency in modern ML frameworks, data pipelines, and cloud infrastructure—and critically, they know how to translate technical decisions for non-specialist stakeholders.

Job responsibilities

• Design, build, and deploy production AI/ML systems that solve real business problems, from data preparation through model inference and monitoring

• Own the full lifecycle of one or more AI/ML projects: problem scoping, architecture, experimentation, validation, and handoff to production

• Partner with data engineers and infrastructure teams to build scalable pipelines, optimize model serving, and maintain system reliability in production

• Lead code reviews, establish best practices for model development and testing, and mentor junior engineers on AI/ML fundamentals and production readiness

• Drive technical decisions around model selection, feature engineering, and performance trade-offs based on business requirements and constraints

• Communicate model capabilities, limitations, and business impact to product and leadership teams; identify and mitigate technical debt and AI-specific risks

• Stay current with advances in AI/ML tooling, techniques, and research; evaluate and prototype emerging approaches for competitive advantage

Candidate requirements

• 5+ years of hands-on AI/ML engineering experience, with at least 2–3 years shipping production systems (not just research, notebooks, or prototypes)

• Deep proficiency in modern ML frameworks (PyTorch, TensorFlow, or equivalent) and strong software engineering fundamentals (version control, testing, CI/CD)

• Demonstrated ability to design and optimize data pipelines, feature stores, and model serving infrastructure; comfortable working with cloud platforms (AWS, GCP, or Azure)

• Track record of solving ambiguous, high-impact problems end-to-end: you've scoped a problem, built a solution, measured its impact, and iterated based on feedback

• Strong communication skills and comfort explaining technical concepts to non-technical stakeholders; proven ability to influence cross-functional teams