Senior MLOps Engineer

Job not on LinkedIn

🕒 July 21

🏄 California – Remote

🏄 California – Remote

💵 $170k - $180k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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Zeitview

WebsiteLinkedInAll Job Openings

51 - 200 employees

🏗️ Construction

📦 Logistics

🏥 Healthcare

💰 $55M Series E on 2023-02

Construction • Logistics • Healthcare

Zeitview is a company dedicated to accelerating the global transition to renewable energy and sustainable infrastructure. They offer advanced inspection solutions that utilize artificial intelligence and machine learning to analyze visual data. Operating in over 60 countries, Zeitview provides efficient inspections and property insights across various asset classes, including wind, solar, construction, telecom, and utilities. Their solutions aim to deliver safe, fast, and cost-effective inspections, helping customers improve operations and reduce maintenance costs while promoting sustainable energy and infrastructure practices.

📋 Description

• Help turn the models built by our ML Scientists, Data Scientists, and Perception Engineers into reliable, production-grade services. • Work on the infrastructure, pipelines, and tooling that take a model or an LLM/agent-backed workflow from a research notebook to a fully monitored deployment running across multiple industry verticals. • Maintain and extend our model registry, building and debugging deployment pipelines and cloud infrastructure, and setting up model and pipeline monitoring and testing. • Troubleshoot issues, such as failed deployments, permissions errors, or inconsistent environments. • Contribute to broader automation initiatives, helping provide deployment visibility and pipeline reliability that let R&D, Software, Product, and Ops teams move in lockstep. • Serve as a key communicator ensuring R&D goals and challenges are well understood by Software Engineering and DevOps teams. • Partner with Scientists, work directly and iteratively with ML Scientists, Data Scientists, and Perception Engineers to translate experimental, research-oriented code into dependable, scalable production services without slowing down their research velocity.

🎯 Requirements

• Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or a related field; typically 4+ years of professional experience in MLOps, ML platform engineering, or infrastructure engineering supporting machine learning teams. • Solid, applied knowledge of MLOps practices, with the ability to work independently across varied production scenarios and escalate only genuinely complex or ambiguous problems. • Demonstrated experience working directly with researchers or ML scientists. You understand research workflows and can translate them into reliable services and productionized models without becoming a bottleneck. • Strong Python skills and solid software engineering fundamentals (testing, code review, version control) • Hands-on experience with a major cloud platform (e.g., AWS), infrastructure-as-code (Terraform), CI/CD tooling (Github Actions), and containerization/orchestration (e.g., Docker, Kubernetes) • Experience building and operating production ML pipelines and model registries, including model versioning and safer release practices (e.g., canary deployments, rollbacks) across environments, as well as coordinating moderately complex, cross-functional infrastructure or deployment projects. • Experience building feedback loops from production back into training data, capturing human corrections as labels and turning retraining into a repeatable pipeline. • Familiarity with experiment tracking, dataset/model versioning, and model documentation practices that support reproducible, auditable ML workflows is a plus. • Familiarity with computer vision or geospatial ML pipelines [Nice to have] • Experience operating LLM/Agentic systems in production, evaluation harness, prompt/tool/retrieval versioning, tracing, token cost optimization [Nice to have] • Experience building data pipelines against relational databases (e.g. PostgreSQL) and API/GraphQL data layers (e.g., Hasura), and integrating external/third-party APIs into production workflows.

🏖️ Benefits

• Your choice of multiple medical insurance plans, including options with an HSA and 100% coverage of the premium for yourself and your dependents • 100% paid dental and vision insurance • Unlimited PTO: We mean it when we say we prioritize work-life balance and mental health • Autonomy and upward mobility • Diverse, equitable, and inclusive culture: a place where your voice matters