Technical Lead (Infrastructure & Security)

| Greater Boston Area
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The Company

We are a young, fast-growing, fully-funded technology company. Our team includes PhDs and graduates from top computer science programs across the country. Before joining Soroco, members of our team worked at Microsoft Research, McKinsey, Sequoia Capital, Goldman Sachs, Citrix, Samsung Research, IBM Research, and more. Collectively, we have published more than 100 papers in journals and conferences, and have been granted over 20 patents. We have flagship offices across three geographies – Boston, London, and Bangalore. We develop intelligent automation systems that augment and repurpose human labor across the modern business enterprise. Soroco is pioneering Trustworthy Automation with patent-pending automation technology designed to automate at an unprecedented scale. Each system is capable of assuming the combined roles of 50-100+ full-time equivalents (FTEs) performing 1000s of complex business rules, thereby creating additional capacity in a business unit. Today, we work with Fortune 500 clients where we have successfully automated large volumes of complex manual effort.

The Role

A Technical Lead in Soroco's Infrastructure & Security team reports directly to Soroco's Head of Infrastructure and Security and is responsible for architecting and engineering some of Soroco's more advanced and large-scale infrastructure systems. As part of these responsibilities, the Technical Lead will lead a team of engineers across some of Soroco's clients to deliver highly reliable systems. The Technical Lead will also periodically engage directly with the client to understand requirements and educate the client on Soroco's infrastructure and security practices. The types of systems the Technical Lead would be responsible for would contain a balance of DevOps, distributed systems, data science, and machine learning (or statistical techniques). The goal is to monitor and analyze large data sets, apply learning techniques to them, and then to automate IT operations based on the learnings at scale. ML experience is not required (Soroco will help train). Responsibilities include (but are not limited to):

  • Applying statistical techniques on large data sets to learn system behavior
  • Build techniques to operate at scale (e.g., on 60,000 new data points a second)
  • Ensuring availability, stability, and reliability of production server technology
  • Leveraging and advancing DevOps tooling to automate various IT operations
  • Influencing and creating designs, architectures, standards, and methods to make distributed systems more reliable
  • Leading a team of engineers that may span across clients
  • Engaging with clients to understand requirements, solve problems, and educate the client * Reviewing code and ensuring high quality standards with strong CI practices

The Candidate

The ideal Software Engineer will have 1-3 years of technical experience with an attitude to learn almost anything, be detail oriented in nature, and have a deep commitment to excellence. Expected Skills:

  • Systematic problem-solving approach, coupled with a strong sense of ownership and drive
  • Experience managing with RHEL or CentOS 7 and Ubuntu LTS Server 14.04 or 16.04
  • Comfortable with data science / statistical techniques (or learning quickly).
  • Familiarity with SQL-based databases and querying large data.
  • Experience with Continuous Integration tooling and maintenance
  • Experience with DevOps tooling such as Ansible, Puppet, or Chef
  • Experience coding in C, C++, Python, Java, and/or Go Bonus Skills
  • Machine learning experience with hands-on experience of relevant toolchains (e.g., PyTorch, pandas, numpy).
  • Understanding of Unix/Linux systems from kernel to shell and beyond, taking in system libraries, file systems, and client-server protocols along the way
  • Networking: knowledge and understanding of network theory, such as different protocols (TCP/IP, UDP, ICMP, etc), MAC addresses, IP packets, DNS, OSI layers, and load balancing)
  • Experience deploying to clouds such as Amazon EC2, Microsoft Azure, or Google Compute Engine
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