Build, manage, and optimize cloud and AI infrastructure across hybrid and multi-cloud environments with CloudBolt. Backed by Insight.
About the role
As a remote-first global SaaS company, CloudBolt is committed to fostering a collaborative, inclusive, and high-performing culture where employees can do their best work and make a meaningful impact.
What they're looking for
- Master's degree or higher in a quantitative field (Computer Science, Machine Learning, Statistics, Applied Mathematics)
- 5+ years of software engineering experience, with at least 3 years building and operating machine learning or statistical systems in production
- Expert-level Python: you write typed, tested, production-grade code, and you're fluent in numpy or similar array-based numerical computing
- Hands-on experience with time-series analysis and forecasting: seasonality, trend decomposition, anomaly detection, and classical statistical methods (percentiles, distributions, smoothing), not just deep learning
- Experience testing ML systems rigorously: regression testing against known-good baselines, behavioral validation, and reasoning about numerical reproducibility
- Working knowledge of Kubernetes: resource requests and limits, autoscaling behavior, and what happens to a workload when it's under-provisioned (OOM kills, CPU throttling)
More about this role
Description
At CloudBolt we help organizations maximize the value of their cloud investments through greater visibility, governance, and cost optimization across complex cloud environments. Our platform empowers teams to make smarter cloud decisions by turning insights into action, helping businesses improve efficiency, control spending, and accelerate innovation.
As a remote-first global SaaS company, CloudBolt is committed to fostering a collaborative, inclusive, and high-performing culture where employees can do their best work and make a meaningful impact.
Learn more at www.cloudbolt.io .
As a Senior ML Engineer , you'll be building and owning the recommendation engine at the heart of our Kubernetes resource optimization product, StormForge, cutting our customers' cloud spend without putting a workload at risk. This is a critical hands-on position at the intersection of applied machine learning and production engineering, where the hardest problems are as much about data quality, guardrails, and knowing when not to recommend as they are about forecasting itself. You'll need to bring rigor and curiosity in equal measure, designing time-series models and the fallback strategies...
Read the full posting on CloudBolt Software | Investment |Insight Partners's site ↗
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