ClimateAI

Senior MLOps Engineer

ClimateAI$170K — $200K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 3-5 years' experience in MLOps or related field with focus on data-intensive systems
  • Familiarity with ML lifecycle management tools like MLFlow or Weights & Biases
  • Proficiency in Infrastructure as Code tools such as Terraform or Pulumi
  • Experience with cloud platforms like AWS, GCP, or Azure at scale
  • Strong communication skills and ability to drive problems to resolution
  • Capacity to collaborate across Data Science, Engineering, and Product teams

Responsibilities

  • Operate and improve the ML model framework for tracking and registry
  • Manage Infrastructure as Code for reproducible and auditable environments
  • Develop CI/CD pipelines for safe ML model deployment
  • Enhance data management systems with observability and retention strategies
  • Create thorough architectural documentation and design proposals

Benefits

  • Competitive salary and equity options
  • Comprehensive medical, dental, and vision benefits
  • Annual learning budget for professional development
  • Unlimited PTO policy with minimum time-off requirements
  • Flexible working hours across many teams
  • Commitment to a culture of diversity and inclusion
  • Opportunity to work on impactful projects addressing climate change
Full Job Description
The Role

As a Senior MLOps Engineer, you will own the infrastructure and ML platform that powers ClimateAi's forecasting and risk products. You will design, build, and operate the cloud systems, data management infrastructure, and model lifecycle tooling that allow our Data Science and ML Engineering teams to develop, compare, register, and ship models with confidence.

This is a high-leverage role at the intersection of infrastructure, ML platform, and security. You will partner closely with Data Science to unblock initiatives like SYO2 and Risk Outlooks model improvements by giving them a real model management platform; with Data Engineering to harden our data lakehouse and pipelines; and with our security lead to provide a strong second engineer on cloud security - building skill duplication across critical systems.

Our hybrid work schedule includes 3 in-person days at one of our core locations (SF, Boston) and 2 remote days.
What You'll Do
  • Stand up and operate the ML model framework that provides ML engineers and data scientists experiment tracking, model registry, and lineage
  • Own and evolve our Infrastructure as Code so environments are reproducible, auditable, and easy for engineers to extend
  • Build CI/CD and deployment patterns for ML pipelines and models, including reproducible training, automated validation, and safe rollouts to production
  • Improve data management systems alongside Data Engineering with storage tiering, lifecycle and retention, cost-performance tradeoffs, and observability across our cloud environments
  • Author clear architectural documentation, runbooks, and design proposals to communicate tradeoffs to engineering and non-technical stakeholders
What We're Looking For
  • 3-5 years of experience in Machine Learning, Backend Software Engineering, Data Engineering, or MLOps roles supporting data-intensive systems in production
  • Production experience with ML lifecycle management platforms such as MLFlow, Weights & Biases, Neptune.ai, Comet.ml or similar
  • Experience with IaC using Terraform, Pulumi, OpenTofu, Encore, Crossplane or similar
  • Deep experience with building systems-of-systems in AWS, GCP, or Azure, that span across multiple services or multiple cloud providers
  • Strong communication and ownership. You scope your work, monitor what you ship, and drive problems to permanent resolution
  • Ability to collaborate closely with Data Scientists, Engineers, and Product, to design and support end-to-end ML workflows.
Bonus Points
  • Experience with training, inference, deploying, and scaling modern ML models to production
  • Experience with configuring models with datasets up to the petabyte-scale
Leveling

This role is posted at our Senior Engineer (P3) level. You will be a self-directed engineer who independently designs, implements, and optimizes complex infrastructure and ML platform components, drives data and model quality, supports cross-functional teams, and provides technical mentorship within the team. Leveling decisions are made in partnership with candidates through the interview process.
Compensation

The base compensation range for employees based in the US is $170,000-200,000 and equity in line with experience and company stage. This salary range may be inclusive of several career levels at ClimateAi and will be narrowed during the interview process based on a number of factors, including the candidate's experience, qualifications, business need and US location.

What We Offer You
  • Competitive salary and equity
  • Medical, dental, vision benefits
  • Learning budget per year
  • Unlimited PTO policy with minimum time off requirements
  • Flexible working hours on many teams
  • Culture of diversity and inclusion including employee resource groups
  • Work with smart, curious, passionate people and be part of the mission to help the world

Culture

At ClimateAi we are driven by a united passion to tackle climate change. We believe in a culture of trust and transparency, where feedback is considered an opportunity for us to contribute to each other's personal and professional growth. We recognize the value of diversity and are an equal-opportunity employer. We hire people who are collaborative, adaptable, communicate well, and love to learn. Expect to give and receive constructive feedback, as we are constantly seeking to push the innovation frontier while simultaneously growing as individuals and as a team.

About ClimateAI

ClimateAI is an artificial intelligence company that provides climate risk analytics for businesses and governments. The company's platform uses machine learning and satellite data to provide real-time insights into climate risks such as flooding, drought, and extreme weather events. ClimateAI's services include risk assessment, scenario planning, and climate adaptation strategies. The company was founded in 2017 and is headquartered in San Francisco, California.
Learn more about ClimateAI
Size
50 employees
Industry
Founded
2017

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