MLO Engineer (Machine Learning Operations)

Stratolaunch; United States; Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

9/80 schedule

Healthcare coverage

Paid parental leave

Matched 401(k)

Tuition reimbursement

Job summary

Stratolaunch is seeking an experienced engineer to build and operate the infrastructure and integrations that make machine learning and AI services useful to engineering, manufacturing, flight operations, and business teams. The work spans model deployment, retrieval systems, monitoring, cloud operations, and developer tools, with scope reflecting experience.

You will deploy production infrastructure for ML workloads, monitor retrieval services and vector search, and create logging, alerting,

Responsibilities

• Deploy and maintain production infrastructure for machine learning workloads and model deployment.
• Monitor and improve retrieval services, vector search, observability, and cost.
• Create logging, alerting, and operational support for AI services.
• Build APIs, plugins, SDKs, and integrations that connect AI capabilities to internal applications.
• Evaluate and integrate tools across software, data, infrastructure, security, and domain teams.

Job description

Role overview

Build and operate the infrastructure and integrations that make machine learning and AI services useful to engineering, manufacturing, flight operations, and business teams. The work spans model deployment, retrieval systems, monitoring, cloud operations, and developer tools, with scope that can reflect the successful candidate’s experience.

Responsibilities

• Deploy and maintain production infrastructure for machine learning, large language model, and retrieval-augmented generation workloads, including cloud-based model inference.
• Monitor and improve retrieval services and vector search, tracking system health, performance, reliability, and cost.
• Create logging, alerting, and observability capabilities, and provide operational support for AI services.
• Build APIs, plugins, SDKs, command-line tools, and integrations that connect AI capabilities to internal applications and workflows.
• Evaluate and integrate commercial, open-source, third-party, and custom tools in collaboration with software, data, infrastructure, security, and domain teams.

Requirements

• Experience designing, deploying, and operating reliable, observable software or machine learning systems in production.
• Background with machine learning workloads, cloud infrastructure, and model deployment; experience with AWS is preferred.
• Proficiency in a programming language such as Python, Rust, C, or MATLAB, or an equivalent language.
• Experience building or integrating APIs, services, plugins, or other software interfaces.
• Familiarity with containers and deployment or orchestration tools, such as Docker, Kubernetes, or equivalent technologies.
• Experience with monitoring, troubleshooting, production support, shell scripting, and CI/CD or DevSecOps practices.

Nice to have

• Experience hosting, monitoring, and optimizing LLM or RAG systems, vector databases, or inference workloads in AWS.
• Familiarity with tools and services such as Qdrant, EC2, Lambda, ECS, EKS, S3, and CloudWatch.

Benefits and work setup

The listing describes a 9/80 work schedule, no required travel, healthcare coverage, paid parental leave, a matched 401(k) contribution, and tuition reimbursement. Salary and level are determined based on factors including location, education, skills, and experience.