Software Engineer (Agentic AI Foundations)

• The Agentic AI Foundations team is building the core platform, systems, and primitives that enable Socure to transition from traditional software workflows to agent-native operations
• As a Software Engineer II on the team, you will help design, build, and harden a secure, evaluable, vendor-agnostic agent platform that teams across Socure can build on, working alongside senior and staff engineers who set the architectural direction
• This is a hands-on, zero-to-one team, and you’ll get outsized exposure to how agentic systems are architected and operated in production
• You’ll bring strong foundational knowledge of LLMs, agentic AI, and GPU/model serving through academic, research, professional, open-source, or other relevant experience, and grow into greater ownership as you build alongside senior engineers on the team
• Build components of a vendor-agnostic agent platform — including orchestration, tool use, memory, and runtime systems — under the guidance of senior engineers on the team
• Implement evaluation and reliability tooling, including metrics, harnesses, and pipelines, to measure and improve agent performance, robustness, and safety in production
• Help implement safety and governance controls, including guardrails, policy enforcement, and human-in-the-loop review mechanisms
• Build data grounding, retrieval, and memory components that keep agents accurate, context-aware, and aligned with Socure’s domain knowledge and policies
• Prototype and iterate on agent behaviors, including planning, multi-step execution, and coordination of tools and services, using real internal workflows as proving grounds
• Partner with product and engineering teams to implement agent-powered workflows using the platform primitives the team builds
• Apply and help refine documented best practices and design patterns for secure, observable, and scalable agent systems
• Bring strong foundational knowledge of LLMs, GPU computing, and model serving to technical discussions and implementation decisions

Benefits

• Health: Comprehensive medical coverage, including vision, dental, and FSA
• Parental Leave: Generous leave allowance for
  parents-to-be
• 401k: Pre-tax savings for retirement
• Life Insurance: Protect your family in
  case of emergency
• PTO: Take as much time as you need,
  when you need it
• 100% Remote: Work from anywhere anytime, whatever suits your lifestyle
• WFH Allowance: Generous reimbursement program to maximize your WFH setup
• Deskpass: Work in an office environment whenever you feel the need
• Mental Health Provider: Connect with clinicians when you need it
• Learning & Development: Self-education allowance & free career coaching
• Employee Assistance Program “EAP”: Lifestyle resources available as well as additional mental health resources
• Fertility Provider: Family planning solutions for those interested
• Personal Legal Support: Significant discounts with RocketLawyer- Very strong foundational knowledge of large language models and agentic AI systems, including architectures, prompting and orchestration patterns, tool use, and evaluation approaches
• 2+ years of professional software engineering experience, with demonstrated experience in distributed systems, backend platforms, infrastructure, or comparable technical environments
• A track record of strong technical performance demonstrated through professional impact, research, challenging technical projects, open-source contributions, internships, or other relevant work
• 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
• Ability to work productively in ambiguous, early-stage problem spaces with guidance from senior engineers, translating direction into working software
• 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
• Solid grounding in distributed systems fundamentals, including concurrency, fault tolerance, observability, and performance
• Strong collaboration and communication skills, with comfort working alongside cross-functional partners such as product, data science, platform, and security
• 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
• 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
• 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
• Exposure to evaluation frameworks, experimentation platforms, or ML systems, such as offline/online evaluations, A/B testing, or agent and model benchmarking
• 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