Sr. Machine Learning 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. Axial Search is a specialist executive search firm built for one kind of hire: leaders who help organizations navigate AI transformation. Apply today to express your interest in roles like this one. Visit our website to learn more about our process and explore free tools for your job search, including our live job market dashboard with salary, skills and hiring trend data from thousands of AI transformation roles. What the market looks like We've tracked 34,500+ mid-level machine learning engineering postings across the US in the last six months, with concentrated hiring in California, New York, and Texas. The market spans technology, financial services, healthcare, and manufacturing — each with distinct ML infrastructure and model-deployment challenges. Median compensation lands around $180,000, with top-tier packages reaching $350,000+.The strongest candidates bring 5+ years of hands-on experience shipping production

ML systems

they're fluent in model training and optimization, comfortable owning data pipelines and infrastructure, and skilled at partnering with data scientists and backend teams to move prototypes into scaled deployments.Job responsibilities Design, build, and ship machine learning models and inference systems in production environments, owning quality, latency, and scalability Lead architecture decisions around feature stores, training pipelines, model serving, and monitoring — balancing accuracy, cost, and operational simplicity Partner with data scientists and product teams to translate research into deployed systems, defining success metrics and managing technical trade-offs

Drive MLOps improvements

build tooling for data versioning, experiment tracking, model registry, and continuous deployment workflows Troubleshoot production ML systems — debugging model performance issues, retraining strategies, and drift detection Contribute to platform and infrastructure decisions that scale ML capabilities across the organization Mentor junior engineers and participate in code review and technical design discussions Candidate requirements 5+ years building and deploying machine learning systems in production — not just experimentation or academia

Strong software engineering fundamentals

API design, testing, version control, and deployment pipelines Hands-on experience with model training frameworks (PyTorch, TensorFlow) and MLOps tools — you've written real training and inference code

Demonstrable experience with data engineering

SQL, distributed data processing, or feature pipeline work Comfortable communicating technical tradeoffs to non-ML stakeholders and working across teams Experience shipping at least one ML system at scale — defining success metrics, monitoring in production, and iterating based on real-world behavior