Senior MLOps Engineer

Job not on LinkedIn

🔥 0 minutes ago

🍂 Massachusetts – Remote

🍂 Massachusetts – Remote

💵 $170k - $180k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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Zeitview

WebsiteLinkedInAll Job Openings

51 - 200 employees

⚡ Energy

🤖 Artificial Intelligence

🚗 Transport

💰 $55M Series E on 2023-02

Energy • Artificial Intelligence • Transport

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. • Partner daily with the R&D team to understand what a model needs to run in production (compute, data inputs, versioning, post-processing). • Coordinate with the Platform and DevOps teams to provision the infrastructure, permissions, and deployment pathways. • Maintain and extend the model registry, build and debug deployment pipelines and cloud infrastructure, and set up model and pipeline monitoring and testing. • Troubleshoot issues, such as failed deployments, permissions errors, or inconsistent environments. • Help shape and document standards for how models move from staging to production. • Ensure R&D goals and challenges are well understood by Software Engineering and DevOps teams.

🎯 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. • 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 • 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