Senior MLOps Engineer I

Zeitview (formerly DroneBase); Boston (MA); On-site; USD 170,000 - 180,000; Full time

Benefits offered by this job

Base salary $170,000–$180,000 USD

Target annual bonus

Stock options

Medical insurance with HSA

Dental and vision insurance

Unlimited PTO

Autonomy and upward mobility

Inclusive culture

Job summary

Zeitview in Boston seeks a Senior MLOps Engineer I to turn ML models into reliable, production-grade services. You will own infrastructure, pipelines, and tooling to move models from research notebooks to monitored deployments across multiple verticals, including model registry, deployment pipelines, and cloud infrastructure.

You will work at the intersection of R&D, Software, and DevOps, partnering with ML Scientists and Platform teams to provision infrastructure, permissions, and deployment

Qualifications

• 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 communicate R&D goals and challenges to Software Engineering and DevOps teams.
• Strong Python skills and solid software engineering fundamentals (testing, code review, version control).
• Hands-on experience with a major cloud platform (e.g., AWS), IaC (Terraform), CI/CD tooling (Github Actions), and containerization/orchestration (Docker, Kubernetes).
• Experience building and operating production ML pipelines and model registries, including model versioning and safer release practices across environments, and coordinating cross-functional infrastructure projects.
• Experience building feedback loops from production back into training data, with experiment tracking and dataset/model versioning; familiarity with model documentation for reproducibility.
• 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, and token cost optimization.
• Nice to have: experience integrating relational databases and external APIs into production workflows.

Responsibilities

• Partner with Scientists: translate experimental code into dependable, scalable production services without slowing research velocity.
• Cross-Functional Collaboration: coordinate with DevOps and Software Engineering on infrastructure requests and data pipelines; support automation initiatives.
• Model Registry, Deployment & Release Management: maintain and improve model registry and deployment pipelines; implement safer release practices.
• Cloud Infrastructure & CI/CD: build and troubleshoot cloud infrastructure and CI/CD pipelines for ML workloads; cost optimization for compute-heavy workloads.
• Monitoring, Drift & Reproducibility: implement monitoring and observability for models and pipelines; track performance and drift; support experiment tracking and dataset/model versioning.
• Ongoing Maintenance & Platform Support: keep deployed ML systems healthy with upgrades, capacity and cost management, data pipeline upkeep, retraining or redeployment.
• Standards & Documentation: define and document conventions for model versioning, deployment promotion, and model documentation/lineage; build self-serve tooling.

Skills

Python

MLOps

AWS

Terraform

CI/CD

Docker

Kubernetes

Model registry

Canary deployments

Rollbacks

Experiment tracking

Versioning

Documentation

Education

Bachelor’s degree in CS/related field

Tools

Terraform

Github Actions

Docker

Kubernetes

AWS

Job description

About the Role

As the Senior MLOps Engineer I, you will help turn the models built by our ML Scientists, Data Scientists, and Perception Engineers into reliable, production‑grade services. You will 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 across multiple industry verticals, including our model registry, deployment pipelines, and the cloud infrastructure our AI/ML platform depends on.

This role sits at the intersection of R&D, Software Engineering, and DevOps. You will work daily with our R&D team to understand what a model needs to run in production (compute, data inputs, versioning, post‑processing), and partner closely with the Platform and DevOps teams to provision necessary infrastructure, permissions, and deployment pathways. You will also contribute to broader automation initiatives, providing deployment visibility and pipeline reliability that let R&D, Software, Product, and Ops teams move in lockstep.

The day‑to‑day will include maintaining and extending our model registry, building and debugging deployment pipelines and cloud infrastructure, and setting up model and pipeline monitoring and testing. You will troubleshoot issues such as failed deployments, permissions errors, or inconsistent environments, and help shape and document standards for how models move from staging to production. Most importantly, you will serve as a key communicator ensuring R&D goals and challenges are well understood by Software Engineering and DevOps teams.

Responsibilities

• 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.
• Cross‑Functional Collaboration: Coordinate with DevOps and Software Engineering teams on infrastructure requests and shared data pipeline needs, and support broader automation initiatives and team goals.
• Model Registry, Deployment & Release Management: Maintain and improve model registry and deployment pipelines, and help implement safer release practices (e.g., shadow deployments, rollback procedures) to reduce risk.
• Cloud Infrastructure & CI/CD: Build, maintain, and troubleshoot cloud infrastructure and CI/CD pipelines that ML workloads run on, working closely with Engineering and DevOps teams on shared tooling, infrastructure‑as‑code, and cost optimization for compute‑heavy workloads.
• Monitoring, Drift & Reproducibility: Implement monitoring and observability for models and pipelines in production, help R&D track model performance and drift over time, and support experiment tracking and dataset/model versioning.
• Ongoing Maintenance & Platform Support: Keep deployed ML systems healthy over time with dependency and infrastructure upgrades, capacity and cost management, data pipeline upkeep, and retraining or redeployment support, and extend support as needs evolve.
• Standards & Documentation: Help define and document conventions for model versioning, deployment promotion, and model documentation/lineage, and build tools to allow scientists and engineers to self‑serve.

Qualifications

• 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. You serve as a key link, communicating R&D goals and challenges to Software Engineering and DevOps teams.
• 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 (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.

What’s Included

• Feel great about your work as you join a leading mission‑driven intelligent aerial imaging company — our goal is to accelerate the global transition to renewable energy and sustainable infrastructure, and you personally will play a large part in making this happen!
• Base salary range of $170,000 - $180,000 USD
• Target annual bonus
• Eligibility for stock options
• 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
• Autonomy and upward mobility
• Diverse, equitable, and inclusive culture: a place where your voice matters

Zeitview is proud to be an equal opportunity employer. At Zeitview, we believe in cultivating an environment where our team members can bring their authentic, whole selves to work. Encouraging identity and belonging is one of the many aspects of our culture that makes us stronger as an organization and drives innovation. We are committed to building and delivering a diverse, inclusive, and equitable workforce that includes age, color, sex, disability, national origin, race, religion or veteran status, that is representative of the world around us, where all individuals are treated with respect and dignity - and to act swiftly if this value is ever threatened. We are constantly striving to be better, and we continue to take strategic steps to advance representation.

We also provide reasonable accommodation for qualified individuals with disabilities and for seriously held religious beliefs in accordance with applicable law.