Senior Machine Learning Engineer

Responsibilities

• Develop and improve machine learning and large language model systems for code generation, unit test generation, debugging, and related software engineering tasks.
• Build AI-powered developer agents and workflows that reason over codebases, interact with development tools, execute multi-step tasks, and support the software development lifecycle.
• Improve model and agent performance through prompt engineering, context engineering, retrieval, fine-tuning, reinforcement learning, tool-use design, and agent harness engineering.
• Build training and evaluation data pipelines, including data collection, filtering, synthesis, labeling, and hard-example mining.
• Apply advances in large language models, code intelligence, program analysis, reinforcement learning, and autonomous software engineering to developer productivity problems.

Requirements

• Bachelor’s degree or above in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or a related field.
• At least 5 years of relevant industry or research experience in machine learning, large language models, code intelligence, AI-powered developer tools, or related areas.
• Strong programming skills in one or more of Python, Go, Java, C++, JavaScript, or TypeScript.
• Understanding of data structures, algorithms, machine learning, operating systems, databases, and fundamental system design principles.
• Experience owning complex machine learning projects end-to-end, including problem formulation, data construction, model development, evaluation, deployment, and production iteration.
• Strong system design and engineering skills, including experience building scalable, reliable, and efficient machine learning systems or AI-powered applications.
• Experience building or leading AI systems for software engineering, such as code generation, coding agents, unit test generation, debugging, or code review, is preferred.
• Deep experience with large language models and model improvement or post-training techniques such as supervised fine-tuning, reinforcement learning, preference optimization, prompt engineering, context engineering, retrieval, or model evaluation is preferred.
• Experience designing agentic systems involving tool-using LLMs, coding agents, agent harnesses, MCP, agent skills, planning, memory, or multi-step execution workflows is preferred.
• Experience building large-scale training data, evaluation datasets, benchmarks, or experimentation pipelines is preferred.
• Understanding of program analysis, compilers, static analysis, code search, repository indexing, testing, and debugging is preferred.
• Publications, patents, open-source contributions, or demonstrated technical leadership are preferred.
• Strong communication and collaboration skills, with the ability to drive complex technical projects across teams.