Senior ML Engineer

• 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 .
• 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 team's 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…?
• 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).
• 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

None flagged.