ML Platform Deployment Engineer
New York, New York, United States of America
Neural Concept
Breaking the trade-off between product quality and development time.About Neural Concept
We build technology that enables engineers to solve complex design challenges faster and more effectively. Our platform leverages 3D deep learning to transform CAE data into real-world results, helping top companies in automotive, aerospace, and energy streamline their R&D.
The Role
We’re looking for a Customer Deployment Engineer to lead and oversee the technical onboarding and integration of our platform in enterprise environments. You’ll play a pivotal role in deploying our software as a SaaS solution on customer cloud infrastructure (AWS, Azure, GCP) or on-premise (HPC, VM). Your work directly contributes to customer satisfaction and long-term platform adoption.
This is a highly customer-facing role. We value engineers who are not only technically capable but also excellent communicators, proactive problem-solvers, and trusted advisors to our customers.
If you're passionate about delivering seamless technical solutions and making a real impact, we'd love to hear from you — apply now!
What You Will Do
Lead Deployments: Design and implement robust deployment architectures that align with customer IT environments and business goals.
Architect Solutions: Assess customer needs and tailor scalable cloud/hybrid/on-premise deployments using Kubernetes, Docker, and cloud-native tooling.
Customer Engagement: Act as a technical point of contact for customers throughout the deployment process, from architecture to troubleshooting.
Security & Compliance: Own the process of responding to IT and security questionnaires. Ensure compliance with enterprise security and privacy requirements.
Collaboration & Feedback: Work closely with internal teams (product, R&D, platform) to continuously improve deployment processes and modularity.
Documentation & Enablement: Produce high-quality documentation, implementation guides, and internal playbooks.
Who You Are
Education: Advanced degree in Computer Science, Engineering, or a related field (or equivalent experience).
Experience: at least 4 years of hands-on experience deploying enterprise software in cloud and/or HPC environments.
Mandatory Technical Skills: Strong background in Kubernetes, Docker, and/or Singularity, with production experience.
Customer-facing Skills: Experience working directly with customers to scope, deploy, and maintain infrastructure.
Cloud Platforms: Proficient with AWS, Azure, or GCP, including security and networking fundamentals.
Tools: Familiar with Terraform, OAuth2, SAML, Slurm, PBS, and monitoring tools like Prometheus and Grafana.
Mindset: A proactive communicator and strategic thinker who thrives in dynamic, interdisciplinary teams.
What We Offer
A competitive salary - we are growing fast and want our employees to benefit from this growth.
Work with a world-class technology team - our engineers are top-notch!
Global Exposure – You will work with leading companies and researchers worldwide
Ongoing training in technologies
Work with people who care about you - we support each other and care about you so that you can thrive.
Hybrid work options and flexible schedules to support work-life balance.
Additional Information
Start Date: ASAP
Location: Jersey City or remote
Travel: Occasional travel to customer sites
Application form
Before you send over your CV, just take a minute to fill out our quick technical evaluation form.It helps us get a better idea of your skills and makes sure we can move forward with your application.
At Neural Concept, we value passion, creativity, and the willingness to learn. If you don’t meet every single qualification but believe you’re a great fit, we encourage you to apply.
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* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Architecture AWS Azure Computer Science Deep Learning Docker Engineering GCP Grafana HPC Kubernetes Machine Learning Privacy R R&D Security Terraform
Perks/benefits: Career development Competitive pay Flex hours
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