Jobgether-posted 8 days ago
$170,000 - $200,000/Yr
Full-time • Mid Level
Remote
11-50 employees

This role offers the opportunity to contribute to a cutting-edge platform that supports the full machine learning lifecycle, from model development and training to deployment and management. You will work on building scalable, high-performance systems that enable organizations to turn data into actionable insights. The position involves close collaboration across engineering, product, and data science teams to deliver robust APIs, enhance model monitoring, and expand capabilities for large-scale AI deployments. The ideal candidate thrives in a dynamic environment, enjoys solving complex distributed computing challenges, and is passionate about driving innovation in enterprise AI platforms. You will play a pivotal role in improving model discoverability, performance, and operational efficiency while shaping the future of AI infrastructure.

  • Develop, maintain, and optimize high-performance back-end systems to support the model development lifecycle and large-scale AI workloads.
  • Design, implement, and maintain secure and scalable APIs for model deployment and integration with enterprise applications.
  • Integrate model monitoring and logging to provide comprehensive visibility into deployment health and performance.
  • Enhance tagging, versioning, and discoverability of models to improve collaboration and operational efficiency.
  • Collaborate with cross-functional teams to incorporate feature requests and improve Domino Apps offerings.
  • Work with cloud platforms and distributed computing frameworks to ensure scalable, reliable, and efficient system performance.
  • Implement robust testing frameworks and maintain CI/CD pipelines to ensure high-quality releases.
  • Proven experience building scalable, high-performance back-end systems in distributed computing environments.
  • Strong proficiency in back-end development languages such as Python, Java, Scala, or Go.
  • Experience designing and developing RESTful APIs, gRPC, or other service integrations.
  • Familiarity with cloud platforms (AWS, Azure, GCP) and container technologies (Docker, Kubernetes).
  • Hands-on experience with performance profiling, optimization, and testing frameworks (unit, integration, end-to-end).
  • Ability to collaborate effectively across engineering, product, and data science teams.
  • Strong problem-solving skills, growth mindset, and ability to work in a dynamic, startup-like environment.
  • Knowledge of ML model deployment, versioning, and lifecycle management tools (e.g., MLflow, KubeFlow) is a plus.
  • Experience with distributed computing frameworks like Apache Spark, Azure ML, or SageMaker is a plus.
  • Competitive base salary: $170,000 — $200,000 USD, with potential equity or performance bonuses.
  • Comprehensive health benefits including medical, dental, and vision coverage.
  • 401(k) retirement savings plan.
  • Wellness stipends and flexible remote work opportunities.
  • Opportunity to work on cutting-edge AI infrastructure with a collaborative, innovative team.
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