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Last checked Nov 1, 2026.

What You’ll Bring

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Machine Learning/AI, or a related field from top tier institutions, or equivalent practical experience demonstrating strong foundations in computer science and machine learning.
• 2+ years of professional software engineering experience, with demonstrated experience in distributed systems, backend platforms, infrastructure, or comparable technical environments.
• Very strong foundational knowledge of large language models and agentic AI systems, including architectures, prompting and orchestration patterns, tool use, and evaluation approaches.
• Strong foundational understanding of GPU computing and model-serving infrastructure, such as CUDA, vLLM, Ollama, LLMLite, TensorRT-LLM, Triton Inference Server, or similar technologies, including the performance and cost trade-offs associated with serving LLMs at scale.
• Solid grounding in distributed systems fundamentals, including concurrency, fault tolerance, observability, and performance.
• Proficiency in at least one modern backend programming language and ecosystem, such as Java, Go, Python, or similar, with comfort working with cloud-native infrastructure, APIs, and data services.
• Ability to work productively in ambiguous, early-stage problem spaces with guidance from senior engineers, translating direction into working software.
• A track record of strong technical performance demonstrated through professional impact, research, challenging technical projects, open-source contributions, internships, or other relevant work.
• Strong collaboration and communication skills, with comfort working alongside cross-functional partners such as product, data science, platform, and security.

Preferred Qualifications

• Experience with multi-agent systems, workflow orchestration, or distributed coordination frameworks through professional work, research, coursework, or technical projects.
• Experience building or using agent platforms — such as orchestration frameworks, tool registries, or memory systems — or LLM routing, caching, or fine-tuning pipelines through professional work, research, internships, open-source contributions, or personal projects.
• Exposure to evaluation frameworks, experimentation platforms, or ML systems, such as offline/online evaluations, A/B testing, or agent and model benchmarking.
• Experience with AI safety, security, or policy systems — including guardrails, policy engines, content filters, or responsible AI frameworks — through professional work, research, coursework, or technical projects.
• Experience with retrieval systems, knowledge graphs, or data platforms used to ground LLMs and agents in enterprise contexts.
• Demonstrated depth in ML systems or LLM infrastructure through professional impact, research, publications, technical projects, competition results, open-source contributions, or comparable experience.

Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly.

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