高级软件工程师 - 仅限加拿大
Senior Software Engineer - Canada Only
Technosylva 是一家全球领先的野火和极端天气风险缓解软件公司。该公司的市场领先解决方案，通过人工智能和机器学习能力进行增强，为电力公用事业、保险和政府机构客户提供实时和预测性洞察。
Technosyla 过去 26 年来一直提供关键解决方案。2022 年，该公司进入快速增长和转型期，获得 TA Associates（一家领先的成长型私募股权公司）投资，员工规模扩大到约 225 人，并在超过 10 个国家提供其产品。2024 年，全球领先的增长型投资者 General Atlantic 宣布对 Technosylva 进行战略增长投资，以支持公司实现其使命。
职位概述
我们正在招聘一名以后端为重点的高级软件工程师，负责构建和优化平台核心的高性能服务和计算模型。该职位结合了强大的系统和后端工程能力，以及先进的数值和并行计算专业知识。您将设计稳健的服务和 API，同时编写高度优化的并发代码，使大规模模拟快速且准确。这是一份适合既擅长服务架构思考，又能从计算和内存密集型工作负载中榨取性能的工程师的理想职位。
职责
- 设计、构建和维护后端服务和 API（REST/gRPC 或等效协议）。
- 建模、优化和维护关系型和非关系型数据库 —— 包括模式设计、查询调优和数据完整性。
- 实现和排查进程间通信（IPC、消息传递和服务间协议）。
- 使用 C# 和 Java 开发主要后端系统。
- 设计和实现高度优化的计算模型，使用 C++。
- 应用同步、并发和多进程技术，最大化吞吐量和正确性。
- 对性能关键代码路径进行分析、基准测试和优化。
- 将数学和算法需求转化为高效、生产级代码。
- 参与架构决策、代码审查和技术指导。
- 编写干净、可测试、文档齐全的代码，并参与 CI/CD 实践。
- 要求
- 必备条件
- 精通 C# 和 Java 后端开发。
- 精通 C++ 高性能计算。
- 熟悉并发、同步原语、线程和多进程。
- 熟悉分布式系统和微服务架构。
- 具备良好的问题解决能力和逻辑思维。
- 有大型系统开发经验者优先。
查看英文原文
About Technosylva
Technosylva is a global leader in wildfire and extreme weather risk mitigation software. The Company’s market-leading solutions, enhanced by AI and machine learning capabilities, provide real-time and predictive insights to support electric utility, insurance and government agency customers.
Technosylva has provided critical solutions for the past 26 years. In 2022 the organization entered a period of significant growth and transformation with investment from TA Associates, a leading growth PE firm, scaling to about 225 employees and offering its product in over 10 countries. In 2024 General Atlantic, a leading global growth investor, announced a strategic growth investment in Technosylva to support the company in its mission.
Role Overview
We are hiring a backend-focused Senior Software Engineer to build and optimize the high-performance services and compute models at the core of our platform. This role combines strong systems and backend engineering with advanced numerical and parallel-computing expertise. You will design robust services and APIs while also writing the highly optimized, concurrent code that makes large-scale simulation fast and accurate. It is an ideal fit for an engineer who is equally comfortable reasoning about service architecture and squeezing performance out of compute- and memory-intensive workloads.
Responsibilities
- Design, build, and maintain backend services and APIs (REST/gRPC or equivalent).
- Model, optimize, and maintain relational and non-relational databases — schema design, query tuning, and data integrity.
- Implement and troubleshoot inter-process communication (IPC, messaging, and service-to-service protocols).
- Develop primary backend systems in C# and Java.
- Design and implement highly optimized compute models in C++.
- Apply synchronization, concurrency, and multi-processing techniques to maximize throughput and correctness.
- Profile, benchmark, and optimize performance-critical code paths.
- Translate mathematical and algorithmic requirements into efficient, production-grade code.
- Participate in architecture decisions, code reviews, and technical mentorship.
- Write clean, testable, well-documented code and contribute to CI/CD practices.
Requirements
Required
- Strong proficiency in C# and Java for backend development.
- Strong C++ skills for high-performance compute work.
- Solid understanding of concurrency, synchronization primitives, threading, and multi-processing.
- Strong mathematical proficiency — numerical methods, linear algebra, or applied math relevant to compute models.
- Experience with API design, database design, and IPC mechanisms.
- Proven ability to profile and optimize compute- and memory-intensive workloads.
Preferred Qualifications
- Experience with parallel and high-performance computing — multithreading at scale, SIMD/vectorization, GPU (CUDA/OpenCL), or distributed compute (MPI).
- Background in scientific, geospatial, simulation, or physics-based modeling domains.
- Familiarity with cloud infrastructure and containerized deployment (AWS/Azure/GCP, Docker, Kubernetes).
- Experience building and operating low-latency, high-throughput data or compute pipelines.
- Working knowledge of observability, testing, and performance-regression practices for production systems.
Originally posted on Himalayas