Senior ML Engineer

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.

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As a Senior ML Engineer youll 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. Youll need to bring rigor and curiosity in equal measure designing time-series models and the fallback strategies around them proving their behavior through repeatable regression testing and iteratively raising the ceiling on accuracy and safety as we discover and learn more about the workloads our customers run.

Responsibilities

• Own the recommendation engine end to end: model selection algorithm design preprocessing and the guardrails that keep recommendations safe to apply to live production workloads.
• Design evaluate and productionize time-series forecasting and statistical models (e.g. Prophet percentile-based estimation) that right-size Kubernetes workloads across CPU memory GPU and JVM heap.
• Build and maintain the data-quality layer: detecting and filtering anomalies load-test windows startup spikes and autoscaling artifacts from production telemetry before it reaches a model.
• Define and continuously improve how we measure recommendation quality: regression testing against golden datasets behavioral validation and accuracy/safety metrics in production.
• Investigate and resolve recommendation quality issues reported from customer environments tracing them through data preprocessing and model behavior.
• Serve as the teams machine learning authority: guide technical direction on ML questions make model-vs-heuristic tradeoff calls and clearly communicate them to platform engineers product and leadership.
• Write production-grade Python for models and pipelines alike and share ownership of the surrounding service (message consumption metrics ingestion caching) with the rest of the team.
• Prototype and validate new optimization capabilities (new resource types new algorithms new workload classes) from research through gradual feature-flagged rollout.
• Stay current on time-series forecasting and resource optimization techniques and pragmatically evaluate which are worth adopting.

Must have strong experience

• Masters 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 youre 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 its under-provisioned (OOM kills CPU throttling).
• Comfort owning a production service not just a model: queues caches retries observability and debugging issues in customer environments from logs and metrics.
• Clear written and verbal communication: as an ML engineer on the team so you must be able to explain model behavior and tradeoffs to platform engineers product managers and customers.

Experience in the following is beneficial

• Experience with Prophet or similar forecasting libraries.
• Prometheus/PromQL and experience working with metrics at scale.
• Cloud cost optimization capacity planning or infrastructure efficiency background
• AWS (S3 Managed Prometheus).
• Experience being the ML domain expert on a team of generalists.

We Offer

Our US benefits package includes:

• Medical/Dental/Vision coverage
• 401k with Company Match
• Health & Dependent Care FSA
• Unlimited PTO
• 11 Company Holidays
• Volunteer/Community Engagement Day
• Tuition Reimbursement
• Paid Parental Leave
• Equity Grants
• Home internet Reimbursement

Base Salary Range: $170000 - $220000 USD Annually actual compensation will be determined based on job-related factors including but not limited to relevant experience skills geographic location internal equity and business needs.

About CloudBolt

CloudBolt is an Equal Opportunity Employer committed to building a diverse and inclusive workplace. We celebrate diversity and do not discriminate on the basis of race color religion sex (including pregnancy childbirth or related medical conditions) sexual orientation gender identity or expression national origin age disability protected veteran status or any other characteristic protected by applicable law.

If you require a reasonable accommodation during the application or interview process please contact .