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# Software Engineer, AI Platform

- **Company:** Lila Sciences, Inc.
- **Location:** Cambridge, MA, United States
- **Experience:** Expert
- **Salary:** $144,000.0 - $240,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Continuous Delivery, Continuous Integration, Software Debugging, Github, Python (Programming Language), Machine Learning, Prometheus, Software Engineering, Data Logging, Grafana, Build Management, Containerization, AI Platforms, Integration Tests, Kubernetes, Infrastructure Automation Frameworks, Machine Learning Operations, Data Pipelines, Software Library
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/software-engineer-ai-platform-cambridge-ma--879f4b10-716a-4d34-b86f-3af24d833457

## About the Role

Software Engineering, Artificial Intelligence (AI), Software Engineering, Machine Learning, Machine Tool, Technical Recruiting, Algorithms, Integration Testing, Continuous Integration, Continuous Deployment/Delivery, * Minimum of 8 years of experience writing software in a commercial setting in Go or Python.

 * Experience implementing scalable software solutions.
 * Experience with MLOps systems, GitOps (ArgoCD, Github Actions)
 * Familiarity with orchestration frameworks (Ray, Argo, Airflow).
 * Proficiency in Containerization, Kubernetes and infrastructure-as-code tools.
 * Acute listening skills and patience to deeply understand complex problems and algorithms.
 * Excellent problem-solving skills and team-first mentality.
 * Energetic self-starter and independent thinker with strong attention to detail.
 * Eager to work with highly skilled and dynamic teams in a fast-paced, entrepreneurial, and technical setting.

Bonus Points For

 * Proficiency with monitoring and logging tools (e.g., Prometheus, Grafana)
 * Research engineering or scientific software experience.

## Description

* Design and build high-performance, secure, and well-documented Machine Learning libraries that implement the algorithms designed by machine learning researchers and engineers.
 * CI/CD pipelines and integration tests for ML workflows.
 * Architect repository designs to follow consistent standards.
 * Support debugging, logging, and maintenance for Ray-based compute environments.
 * Data ingestion pipelines linking lab data to ML teams.