Lead Software Engineer

Skills You’ll Need: Minimum Qualifications

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Computer Engineering, Math, or a related technical field.

• Proficiency in C or C++ programming on Linux, demonstrated through coursework, academic research, internships, open-source contributions, or personal projects.

• Solid understanding of data structures, algorithms, computational complexity, and object-oriented programming.

• Strong analytical and problem-solving skills, with the ability to break down complex technical problems and develop logical, structured solutions.

• Basic understanding of software debugging and testing principles.

• Familiarity with coding agents and their application to software development tasks such as codebase exploration, implementation, debugging, test development, and documentation.

• Ability to review and validate agent-generated results, with an understanding of their correctness and broader technical implications.

• Willingness and ability to learn unfamiliar technologies, engineering tools, and technical domains.

• Ability to work effectively in a collaborative engineering environment.

• Clear written and verbal communication skills.

Preferred Qualifications

• Sound technical judgment, with attention to correctness, simplicity, performance, maintainability, and overall software quality.
• Experience or knowledge in one or more of the following areas is preferred.
• Relevant experience may be demonstrated through coursework, academic research, internships, open-source contributions, technical competitions, or personal projects:

• Algorithms and optimization, including graph algorithms, partitioning, clustering, matching, flow and cut algorithms, heuristics, metaheuristics, or combinatorial optimization.

• Parallel and concurrent programming, including multithreading, synchronization, task scheduling, distributed computation, SIMD programming, vectorization, or GPU acceleration.

• Performance engineering, including profiling, memory optimization, cache-aware data structures, workload analysis, or algorithmic tuning.

• Systems programming, computer architecture, operating systems, compilers, or large-scale data processing.

• Electronic Design Automation, digital logic design, simulation, emulation, verification, or semiconductor design workflows.

• Experience with modern software development workflows and tools on Linux, including version control systems such as Git or Perforce; build tools and systems such as Make and CMake; testing frameworks such as GoogleTest; and practices such as continuous integration, static analysis, and code review.

• Hands-on experience applying coding agents to multi-step software engineering tasks, such as repository exploration, technical planning, implementation, build execution, test development, debugging, validation, and iterative refinement.

• Understanding of Agentic Software Engineering, including the disciplined integration of coding agents, deterministic engineering tools, objective validation mechanisms, and human engineering judgment.

• Familiarity with Harness Engineering, including the context, tools, execution environments, permissions, validation mechanisms, feedback channels, and operational guardrails that enable coding agents to operate reliably.

• Familiarity with Loop Engineering, including iterative agent execution, progress evaluation, validation checkpoints, retry and recovery strategies, and explicit completion or stopping criteria.

• Familiarity with Context Engineering, including structured task specifications, repository-level instructions, relevant context selection, progressive context retrieval, and context management across multi-step engineering tasks.

• Exposure to Agent Orchestration, including tool invocation, task decomposition, workflow coordination, sub-agent collaboration, and integration between model-driven reasoning and deterministic engineering tools.

• Experience developing or configuring Agent Skills, reusable workflows, tool integrations, or MCP-based capabilities that encode domain knowledge and support the repeatable execution of engineering tasks.

• Familiarity with Human-in-the-Loop Control, including review checkpoints, approval boundaries, permission management, uncertainty handling, and escalation mechanisms.

• Experience using engineering validation techniques such as compilation, automated testing, regression analysis, static analysis, runtime diagnostics, or profiling to provide objective feedback on agent-generated software changes.

• Demonstrated curiosity about emerging software engineering technologies, along with the ability to learn through hands-on experimentation and empirical validation.