Staff Software Engineer, Machine Learning

Staff Software Engineer, Machine Learning (LLM Backend)

Location: Burlingame, CA (On-site, 4 days/week)

Employment Type: Full-time

Salary Range: $145K - $250K

We are a rapidly growing AI technology company transforming mission-critical processes in the healthcare industry. We are seeking a Staff Software Engineer to architect and scale the secure, reliable backend systems that power our advanced Large Language Model (LLM) applications. This role requires expertise at the intersection of applied AI, distributed systems, and security/compliance in a regulated domain.

You will be responsible for pushing LLMs beyond demos into mission-critical, cost-efficient, and secure production healthcare workflows. This is a technical leadership role driving the end-to-end delivery of core ML/LLM systems.

Key Responsibilities:

• Backend for LLMs: Architect and implement scalable, low-latency APIs and services that wrap, orchestrate, and optimize LLMs for high-volume healthcare use cases (e.g., automated voice AI agents).
• Data & Retrieval Pipelines: Build robust ingestion, preprocessing, and Retrieval-Augmented Generation (RAG) pipelines to ground LLMs in proprietary clinical and revenue-cycle data.
• LLMOps & Observability: Design and implement systems for model monitoring, evaluation harnesses, cost tracking, and guardrails, ensuring reliability and responsible use in production.
• Performance & Optimization: Engineer solutions for caching, batching, load balancing, and scaling LLM workloads across cloud and containerized environments.
• Security & Compliance: Implement HIPAA-ready infrastructure, robust data governance, and auditability for all LLM-powered applications in a regulated setting.
• Technical Leadership: Drive end-to-end delivery of LLM backend projects, establish engineering best practices, and mentor peers in complex LLM system design.

Requirements

• Experience: 5+ years of backend or full-stack software engineering experience, with 3+ years working specifically on ML/LLM-enabled applications.
• Coding Proficiency: Strong coding skills in Python and ideally experience with one statically typed language (e.g., Go, Java, or TypeScript).
• LLM Integration: Experience with LLM integration frameworks (e.g., Hugging Face, LangChain, LlamaIndex, or direct APIs from OpenAI/Anthropic).
• Cloud-Native Expertise: Deep knowledge of distributed systems, service-oriented architecture, and building APIs at scale. Hands-on experience with cloud platforms (AWS/GCP/Azure), Kubernetes, Docker, and Terraform.
• System Design & LLMOps: Excellent system design skills and familiarity with MLOps/LLMOps practices (CI/CD for models, evaluation, monitoring).
• Location: Must be available to work 4 days a week on-site in the Burlingame office.

Preferred Qualifications:

• Experience applying LLMs in healthcare or other regulated industries (e.g., familiarity with FHIR, HL7, HIPAA).
• Hands-on experience with RAG pipelines, vector databases, and structured-output orchestration.
• Background in enterprise SaaS or mission-critical platforms where uptime and strict compliance are paramount.

Benefits

• Highly competitive base salary ($145K - $250K) based on experience and qualifications.
• Equity Options as part of the total compensation package.
• Comprehensive Benefits: Health, dental, and vision coverage, and 401(k) plan.
• Unlimited PTO policy.
• Visa Sponsorship is available.
• A leadership role with ownership over core AI systems in a domain that drives significant, real-world impact.

Due to the high volume of applications we anticipate, we regret that we are unable to provide individual feedback to all candidates. If you do not hear back from us within 4 weeks of your application, please assume that you have not been successful on this occasion. We genuinely appreciate your interest and wish you the best in your job search.

Commitment to Equality and Accessibility:

At MLabs, we are committed to offer equal opportunities to all candidates. We ensure no discrimination, accessible job adverts, and providing information in accessible formats. Our goal is to foster a diverse, incl