Senior Software Engineer, Productization & AI Systems
Role details
Job location
Tech stack
Job description
Productization & Technical Maturation
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Lead the technical maturation of advanced prototypes into production-ready systems, identifying gaps in architecture, scalability, reliability, and usability.
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Define and drive technical roadmaps that align product features with customer requirements.
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Identify and implement variability points to enable rapidly tailoring solutions to the needs of different customers.
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Establish criteria for MVPs, beta releases, and production readiness.
System Architecture & Design
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Lead planning and implementation of system architectures for AI-enabled software systems, including APIs, services, data pipelines, and deployment infrastructure.
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Make informed tradeoffs between research flexibility and product stability, performance, and maintainability.
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Guide refactoring of research codebases into modular, reusable, and testable components.
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Collaborate with UI/UX designers to understand and incorporate end user interaction needs into architecture decisions.
AI Engineering & MLOps
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Design and implement workflows for training, evaluation, deployment, and monitoring of AI/ML models.
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Establish MLOps practices including versioning, reproducibility, CI/CD, performance monitoring, and lifecycle management.
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Work closely with researchers to transition experimental models into operational pipelines.
Deployment Operations
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Lead containerized deployments using Docker and related tooling; support cloud and on-premise environments.
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Apply modern DevSecOps practices to improve system reliability, security, observability, monitoring, logging, and operational diagnostics.
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Anticipate operational risks and design mitigations for real-world usage.
Requirements
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Bachelor's degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience).
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Ability to obtain and maintain a U.S. Government security clearance.
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6+ years [LC1] of professional experience in software engineering, with demonstrated ownership of complex systems.
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Strong proficiency in Python and experience with production-quality software development.
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Demonstrated experience transitioning prototypes or early-stage systems into production or near-production environments.
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Strong understanding of AI/ML systems, including model integration, evaluation, and deployment considerations.
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Hands-on experience with containerization (Docker) and modern deployment workflows.
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Ability to lead technical planning, break down ambiguous problems, and drive execution across teams.
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Strong communication skills and comfort working with researchers, engineers, and external stakeholders.
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Willingness to travel to support integration, deployment, or customer activities.
Nice to Have
- Experience with MLOps frameworks, CI/CD pipelines, and model lifecycle management.