Lead AI Engineer

About the role

Axial Search is a specialist executive search firm built for one kind of hire: leaders who help organizations navigate AI transformation.

What The Market Looks Like

We've tracked 34,500+ mid-level AI/ML engineering postings in the US over the last six months, with strong demand concentrated in Technology, Professional Services, and Financial Services—and particularly in California, New York, and Texas. Compensation for this cohort typically ranges from $160,000 to $350,000, with a median near $180,000. The strongest candidates bring hands-on experience shipping production ML systems, a track record of collaborating effectively across research and engineering teams, and the ability to balance technical depth with business impact.

Job Responsibilities

• Design, develop, and deploy machine learning models and systems that solve real business problems, from ideation through production and monitoring
• Build and optimize data pipelines, feature engineering infrastructure, and model training workflows to support model development at scale
• Partner with data scientists, product managers, and infrastructure teams to understand requirements, scope technical feasibility, and integrate ML systems into production applications
• Own model performance, debugging, and iteration cycles—diagnosing failures, running experiments, and refining approaches based on production metrics
• Lead code reviews, document technical decisions, and establish engineering practices that make ML systems maintainable and reproducible
• Drive adoption of ML tooling, frameworks, and best practices within the team—including MLOps infrastructure, versioning, and deployment automation
• Contribute to technical architecture and strategy discussions around scalability, latency, and resource constraints as ML workloads grow

Candidate Requirements

• 5–8 years of production ML engineering experience—ship and maintain real models in live systems, not research or prototyping alone
• Strong proficiency in Python and common ML frameworks (PyTorch, TensorFlow, scikit-learn); experience with data processing tools and SQL
• Demonstrated ability to move fluidly between model development and software engineering—comfortable with testing, version control, and deployment pipelines
• Experience building and scaling data pipelines, feature stores, or model serving infrastructure; familiarity with MLOps practices and tooling
• Proven track record working cross-functionally—collaborating effectively with data scientists, engineers, and product stakeholders to ship models that drive business value
• Strong communication skills; ability to explain technical decisions and tradeoffs clearly to both technical and non-technical audiences