About This Job

About This Job Position Overview

We are seeking a Machine Learning Engineer to join our team developing machine learning solutions for quality assurance and process monitoring in additive manufacturing. Working closely with process engineers, software engineers, and fellow ML engineers, you will develop and deploy models using image, time-series, and machine log data from advanced manufacturing systems.

Prior experience in additive manufacturing or 3D printing is not required. We are particularly interested in candidates with scientific, engineering, or technical backgrounds who have applied machine learning to complex real-world problems involving sensor data, physical systems, or experimental datasets, and who enjoy working closely with domain experts to deliver practical, high-impact solutions.

Velo, Velo3D, Sapphire and Intelligent Fusion are registered trademarks of Velo3D, Inc. Without Compromise, Flow, Flow Developer, and Assure are trademarks of Velo3D, Inc.

With the only SupportFree laser powder bed fusion capability, we enable on-demand manufacturing of production quality Titanium, Inconel, and Aluminum parts with an unprecedented degree of design freedom and quality control. The VELO3D award-winning solution includes an integrated offering of hardware and software: Sapphire® metal AM production printer, Flow™ print preparation software, Assure™ quality assurance and control system, and an integrated manufacturing process that runs throughout the printing operation.

Our team enjoys excellent benefits including healthcare coverage and 401(K) employer contributions. We believe in transparency and recognizing exceptional efforts through our monthly all-hands meetings and team member appreciation awards.

Our job titles may span more than one career level. The starting base salary for this full-time position is between $150,000 and $220,000. This salary range reflects the minimum and maximum target for this position in the U.S. The actual base pay is dependent upon many factors, such as work experience, job-related skills, related education, work location, and market demands. The base pay range is subject to change and may be modified in the future. In addition to a competitive base salary and a comprehensive benefits package, this position may be eligible for other forms of compensation such as participation in a bonus and equity program, as applicable.

Velo3D provides equal employment opportunities to all employees and applicants for employment without regard to, and prohibits discrimination and harassment based on, race, color, religion, age, sex, national origin, disability, medical condition, genetic information, military or veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Position Name Machine Learning Engineer

Position Name

Machine Learning Engineer

Role Responsibilities

• Develop ML models using in-process sensor data to identify anomalies and quality issues during printing.
• Build and iterate on training and evaluation workflows; document experiments and results for reproducibility.
• Own ML experimentation end to end: Design datasets, preprocessing pipelines, and training workflows; iterate on model architectures and metrics; document experiments and results for reproducibility.
• Help define data collection and management: Partner with process and software teams to improve how build data is ingested, cataloged, versioned, and made available for training and evaluation.
• Deploy models into production: Work with print software and embedded teams to integrate validated models into production code running on printer hardware, including performance and reliability considerations.
• Collaborate with supporting software engineers: Hand off validated Python prototypes for production hardening, provide clear specifications and acceptance criteria, and support integration and regression testing.

Qualifications

Must-have

• Bachelor's degree in Computer Science, Electrical Engineering, Applied Mathematics, or a related field; advanced degree preferred.
• 3+ years of experience building and evaluating machine learning models in a professional setting.
• Hands-on experience with computer vision or image-based ML (e.g., segmentation, classification, or anomaly detection).
• Strong Python skills and experience with modern ML frameworks (e.g., PyTorch).
• Experience designing ML pipelines: data loading, preprocessing, training, evaluation, and experiment tracking.
• Comfort working in a production software environment: version control, code review, testing, and cross-functional collaboration.
• Ability to communicate technical tradeoffs clearly to engineers and non-engineers.
• Strong programming skills in Python or C++.
• Experience organizing and working with structured and unstructured datasets.
• Background in a STEM or scientific discipline, with demonstrated use of ML to address substantive technical or engineering problems.
• Bonus
• Experience with powder bed fusion or other additive manufacturing processes.
• Knowledge of manufacturing data workflows, IoT sensor data, or industrial automation systems.
• Experience with image-based or time-series machine learning.
• Familiarity with model deployment in production or embedded environments.
• Familiarity with cloud storage and data pipelines (e.g., AWS S3, batch retrieval workflows).
• Experience in domains such as robotics, aerospace, materials, instrumentation, scientific computing, or other fields where ML is applied to physical or experimental data.