{"id":13108,"date":"2025-10-27T19:45:38","date_gmt":"2025-10-27T19:45:38","guid":{"rendered":"https:\/\/kick-start.us\/?post_type=job_listing&#038;p=13108"},"modified":"2025-10-27T19:45:38","modified_gmt":"2025-10-27T19:45:38","slug":"california-398-staff-software-engineer-machine-learning","status":"publish","type":"job_listing","link":"https:\/\/kick-start.us\/ko\/vaga\/california-398-staff-software-engineer-machine-learning\/","title":{"rendered":"Staff Software Engineer, Machine Learning"},"content":{"rendered":"<p>\uc804\uccb4 \uc9c1\ubb34 \uc124\uba85<br \/>Staff Software Engineer, Machine Learning\u00a0<\/p>\n<p>Location:\u00a0Burlingame, CA (On-site, 4 days a week)<\/p>\n<p>Compensation:\u00a0$145,000 -$250,000 (Base Salary) + Equity Options<\/p>\n<p>We are a rapidly growing AI company applying\u00a0Large Language Models (LLMs)\u00a0to transform a critical, high-impact domain within the\u00a0healthcare industry. We&#8217;re looking for a highly skilled and experienced\u00a0Staff Software Engineer, Machine Learning\u00a0to drive the architecture and scaling of our LLM backend systems.<\/p>\n<p>This role is ideal for an engineer who thrives at the intersection of backend architecture and applied AI, designing the APIs, data pipelines, and infrastructure that make LLMs reliable, secure, and cost-efficient in production. Join us and help push LLMs beyond demos into\u00a0mission-critical healthcare workflows.<\/p>\n<p>Job Description \ufe0f\u00a0<\/p>\n<p>As a Staff Software Engineer, you\u2019ll be a technical leader responsible for the end-to-end delivery of our core LLM backend projects, setting the engineering best practices, and mentoring peers in system design.<\/p>\n<p>Key Responsibilities:<\/p>\n<ul>\n<li>Backend for LLMs: Architect and implement scalable, low-latency APIs and services that wrap, orchestrate, and optimize LLMs for complex, regulated use cases.<\/li>\n<li>Data &amp; Retrieval Pipelines: Build ingestion, preprocessing, and Retrieval-Augmented Generation (RAG) pipelines to ground LLMs in specialized clinical and revenue-cycle data.<\/li>\n<li>LLMOps &amp; Observability: Design systems for model monitoring, evaluation, cost tracking, and guardrails, ensuring reliability and responsible use in a production environment.<\/li>\n<li>Performance &amp; Optimization: Engineer solutions for caching, batching, load balancing, and scaling LLM workloads across cloud and containerized environments.<\/li>\n<li>Security &amp; Compliance: Implement HIPAA-ready infrastructure, data governance, and auditability for all LLM-powered applications.<\/li>\n<li>Cross-Functional Collaboration: Partner closely with product managers, ML engineers, and domain experts to translate business workflows into robust backend systems.<\/li>\n<li>Technical Leadership: Drive the technical direction, establish engineering excellence, and mentor team members in advanced system design.<\/li>\n<\/ul>\n<p>\uc694\uad6c \uc0ac\ud56d<\/p>\n<ul>\n<li>Experience: 5+ years of backend or full-stack software engineering experience, with 3+ years specifically working on ML\/LLM-enabled applications.<\/li>\n<li>Coding Skills:\u00a0Strong expertise in\u00a0Python\u00a0(and ideally one statically typed language such as Go, Java, or TypeScript).<br \/>LLM Ecosystem: Experience with LLM integration frameworks (e.g., Hugging Face, LangChain, LlamaIndex, OpenAI APIs, Anthropic).<\/li>\n<li>Architecture: Deep knowledge of distributed systems, service-oriented architecture, and building APIs at scale.<\/li>\n<li>Cloud Native: Hands-on expertise with cloud-native technologies (AWS\/GCP\/Azure), Kubernetes, Docker, and infrastructure-as-code (e.g., Terraform).<\/li>\n<li>Practices: Familiarity with MLOps\/LLMOps practices: CI\/CD for models, robust evaluation harnesses, monitoring, and reproducibility.<\/li>\n<li>System Design: Excellent system design skills and the ability to align complex technical architecture with critical product goals.<\/li>\n<\/ul>\n<p>Preferred Qualifications:<\/p>\n<ul>\n<li>Experience applying LLMs in healthcare or other highly regulated industries (e.g., familiarity with FHIR, HL7, HIPAA).<\/li>\n<li>Hands-on experience with advanced RAG pipelines, vector databases, and structured-output orchestration.<\/li>\n<li>Background in enterprise SaaS or mission-critical platforms where uptime, low-latency, and scale are paramount.<\/li>\n<\/ul>\n<p>\ud61c\ud0dd<\/p>\n<ul>\n<li>High-Impact Work: The unique opportunity to apply cutting-edge Generative AI to one of the world&#8217;s most important and complex industries\u2014healthcare.<\/li>\n<li>Leadership Ownership: A key leadership role with significant ownership over core ML\/LLM systems and direct influence on the company&#8217;s technical direction.<\/li>\n<li>Financial Package: Competitive base salary, generous equity options, and comprehensive benefits.<\/li>\n<li>Health &amp; Wellness: Full medical, dental, and vision coverage.<\/li>\n<li>Retirement: Access to a 401k plan.<\/li>\n<li>Time Off: Flexible and Unlimited PTO policy.<\/li>\n<li>Location &amp; Collaboration: Work in our Burlingame, CA office four days a week, fostering deep collaboration with a dedicated, low-ego, and adaptive team.<\/li>\n<li>Visa Sponsorship: Visa sponsorship is available for this position.<\/li>\n<\/ul>\n<p>\uc9c0\uc6d0\uc790 \uc218\uac00 \ub9ce\uc744 \uac83\uc73c\ub85c \uc608\uc0c1\ub418\uc5b4 \ubaa8\ub4e0 \uc9c0\uc6d0\uc790\uc5d0\uac8c \uac1c\ubcc4\uc801\uc73c\ub85c \ud53c\ub4dc\ubc31\uc744 \ub4dc\ub9ac\uc9c0 \ubabb\ud558\ub294 \uc810 \uc591\ud574 \ubd80\ud0c1\ub4dc\ub9bd\ub2c8\ub2e4. \uc9c0\uc6d0 \ud6c4 4\uc8fc \uc774\ub0b4\uc5d0 \uc5f0\ub77d\uc744 \ubc1b\uc9c0 \ubabb\ud558\uc2e0 \uacbd\uc6b0, \uc774\ubc88 \ucc44\uc6a9\uc5d0 \ud569\uaca9\ud558\uc9c0 \ubabb\ud55c \uac83\uc73c\ub85c \uac04\uc8fc\ud574 \uc8fc\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4. \uad00\uc2ec\uc744 \uac00\uc838\uc8fc\uc154\uc11c \uc9c4\uc2ec\uc73c\ub85c \uac10\uc0ac\ub4dc\ub9ac\uba70, \uad6c\uc9c1 \ud65c\ub3d9\uc5d0 \ucd5c\uc120\uc744 \ub2e4\ud558\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n<p>\ud3c9\ub4f1\uacfc \uc811\uadfc\uc131\uc744 \uc704\ud55c \ub178\ub825:<\/p>\n<p>MLabs\ub294 \ubaa8\ub4e0 \uc9c0\uc6d0\uc790\uc5d0\uac8c \ub3d9\ub4f1\ud55c \uae30\ud68c\ub97c \uc81c\uacf5\ud558\uae30 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