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Software Engineer
3 months ago
As software engineer at Phinyx AI Inc., you will help build bleeding edge technologies at the intersection of generative AI and scientific computing. You’ll join a world-class team running at a startup pace, publishing code with high standards of implementation, security, privacy, and reliability. Currently, the nature of tasks is diverse (full stack) with variation between sprints.
Location: Greater Boston-Providence, RI Preferred
We are seeking a candidate preferably based in the greater Boston area willing to commute periodically to Providence, the location of our core operations, other developers and facilities. However, exceptional candidates located elsewhere will also be considered.
Responsibilities
- Advance the design and development of the software architecture, considering:
Scaling cloud compute units
On-prem deployment
Security and privacy
Performance / accelerations
- Define and execute roadmap for Software / DevOps activities
- Ensure enterprise-grade software, dictate high standards
- Build and maintain top performance ML pipelines
Qualifications
Essential
- Production environment experience: Proven track record of publishing code to production. Experience with release trains at a certain cadence, delivering on time and with high quality. At least 8 years of experience expected.
- Cloud Computing: In depth knowledge of and experience with scaling computing across cloud and on-prem. Strong understanding of different cloud architectures and impact on security, privacy, monitoring, databases, etc.
- ML DevOps: Experience being responsible for DevOps for ML pipelines. Able to debug runtimes and optimize architectures.
Preferred
- ML Ops: Experience in developing machine learning products, understanding the fundamentals of neural networks and being able to implement them successfully.
- Microsoft Azure: Specific experience with this platform.
- Generative AI: Experience in developing generative AI products. Proven record of being part of the development pipeline of such solutions and the intricacies of using generative systems.
- Scientific machine learning: Experience with coding and testing physics informed machine learning methods, operator learning methods, and similar techniques.
- Scientific computing: Experience with coding and testing numerical methods such as finite elements, multigrid, reduced order modeling and similar techniques.
