Senior MLOps Engineer

Zeitview

Senior MLOps Engineer turning ML models into reliable, production-grade services at Zeitview. Collaborating with scientists and engineers on infrastructure, pipelines, and automation initiatives.

Posted 7/21/2026full-timeRemote • California • 🇺🇸 United StatesSenior💰 $170,000 - $180,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in MLOps practices, building and operating production ML pipelines, and translating research workflows into scalable services. Proficient in Python, cloud infrastructure, and CI/CD tooling to ensure reliable deployment and monitoring of machine learning models.

Highest-signal resume keywords

MLOps PracticesPython ProgrammingCloud Infrastructure (AWS)CI/CD Tooling (Github Actions)Containerization/Orchestration (Docker, Kubernetes)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

MLOpsProduction ML PipelinesModel RegistriesVersion ControlTestingInfrastructure-as-Code (Terraform)Experiment TrackingDataset/Model VersioningData PipelinesRelational Databases (PostgreSQL)

Soft Skills

CommunicationCollaborationProblem-Solving

Tools & Technologies

AWSTerraformGithub ActionsDockerKubernetesHasura

Industry Keywords

Machine LearningProduction ServicesDeployment PipelinesMonitoringComputer VisionGeospatial MLLLM/Agentic Systems

Tech Stack

Tools & technologies

AWSCloudDockerKubernetesPythonTerraform

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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