Principal Machine Learning Engineer (Manufacturing AI)

Location: New York, United States; hybrid model with time in the New York office, work-from-home days, and travel to customer sites worldwide for an average of 3–4 weeks per quarter.

Salary: $140,000–$190,000 per year, depending on experience.

Company

hirify.global is a physics AI company building simulation software and AI-enabled engineering tools for industrial manufacturing, aerospace and defence, automotive, semiconductors, materials, and energy and renewables.

What you will do

• Own the ML approach for manufacturing and MRO data from modelling through deployment in customer production environments across cloud, on-premise, and edge.
• Build end-to-end training, inference, data pipelines, and APIs for sparse, drifting, miscalibrated process and sensor data, including 3D point-cloud and mesh data.
• Select between physics simulation, reduced-order, and learned models, and fuse simulation outputs with shop-floor measurements.
• Set uncertainty quantification and validation standards, design experiments, and assess whether data is sufficient for training.
• Define manufacturing and MRO product requirements, lead data and ML architecture, and communicate trade-offs to senior stakeholders and process owners.
• Mentor engineers and data scientists, support project scoping, and help bring new customers to hirify.global.

Requirements

• At least 5 years of industry experience after a Master’s or PhD in a commercial, non-research environment.
• Production experience building and deploying ML on industrial manufacturing process or sensor data, such as parameter logs, sensor traces, inspection records, or small DoE sets.
• Experience shipping ML systems end to end and at scale, including reliable and scalable data pipelines, training systems, inference systems, and APIs.
• Ability to work with sparse, drifting, miscalibrated, and partly unrecorded equipment data and judge whether it is sufficient for modelling.
• Experience fusing simulation output with shop-floor measurements where they disagree.
• Experience setting uncertainty quantification and validation standards and designing experiments where machine time is the cost.
• Ability to transfer models across machines, material lots, and consumable batches.
• Technical leadership experience, including architecture, product or tooling decisions, stakeholder communication, and mentoring.
• Experience with Python, PyTorch, Pandas, FastAPI, SciPy, Kubeflow, or comparable tools; partial stack coverage is acceptable.

Nice to have

• Experience in aerospace or another regulated manufacturing environment.

Culture & Benefits

• Hybrid work combining New York office time with work-from-home days and a sustainable working pace.
• Flat structure where ideas are evaluated independently of hierarchy, with an emphasis on collaboration and continuous learning.
• Equity options and a 5% 401(k) contribution.
• Private health insurance, FSA, subsidized Gympass/Wellhub, and a weekly team lunch.
• Twenty days of annual leave plus public holidays, enhanced parental leave, and personal development support.
• Regular customer-site travel is required, averaging 3–4 weeks per quarter.