Senior Software Engineer – Applied AI
| Company | Flagship Pioneering |
|---|---|
| Location | Cambridge, MA, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s |
| Experience Level | Senior |
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- 5+ years of experience successfully building and deploying scalable software systems in production environments.
- Cloud & DevOps Knowledge: Hands-on experience with AWS, GCP, or Azure; strong understanding of containerization (Docker/Kubernetes), infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines.
- Workflow Orchestration: Familiarity with modern orchestrators (Temporal, Airflow, etc.) to coordinate complex data and ML pipelines.
- Data Engineering: Ability to design robust data flows and handle large volumes of structured/unstructured data, including performance optimization.
- Communication & Collaboration: Proven track record of working cross-functionally with scientists, data engineers, and product teams; able to explain complex ideas to diverse audiences.
Responsibilities
- Lead End-to-End Implementation: Drive the technical design and implementation of AI systems, creating a scalable, extensible framework that powers iterative R&D workflows.
- Contribute to Agentic AI Platform: Design, develop, and maintain systems leveraging large language models for intelligent decision-making. Orchestrate workflows, gather data, and refine scientific designs.
- Architect & Implement ML Pipelines: Build and maintain end-to-end data and machine learning pipelines to facilitate a robust data environment for agentic interactions.
- Collaborate Cross-Functionally: Partner with domain scientists, ML engineers, and product leads to integrate various technologies—ML models, data/compute infrastructure, and experimental automation tools.
- Develop Best Practices for Agentic Systems: Establish design patterns for planning, tool selection, and evaluation, ensuring high-performance outcomes.
- Optimize Performance & Reliability: Profile and optimize agentic workflows for throughput, fault-tolerance, and cost efficiency. Implement logging, monitoring, and alerting to ensure production readiness.
- Champion Best Practices: Set standards for code quality, testing, and documentation. Mentor junior engineers and foster a culture of knowledge sharing.
Preferred Qualifications
- Experience in AI/ML Pipelines: Prior work building or optimizing ML pipelines (training, evaluation, deployment); experience monitoring model performance in production.
- Hands-On Familiarity with Latest AI Concepts: Exposure to AI technologies such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or agentic frameworks—and how they integrate into a software platform.
- Domain Background: Exposure to life sciences, material sciences, or related fields.
- Technical Leadership: Experience leading or mentoring a team and making key architecture decisions.