Senior ML Engineer
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Job Description
Senior ML Engineer building CloudBolt’s recommendation engine for Kubernetes resource optimization. Reducing SaaS customers’ cloud spending through safe forecasting and production ML systems.
Responsibilities:
- Own the recommendation engine end to end, including model selection, algorithm design, preprocessing, and safety guardrails
- Design, evaluate, and productionize time-series forecasting and statistical models for right-sizing Kubernetes workloads across CPU, memory, GPU, and JVM heap
- Build and maintain the data-quality layer by detecting and filtering anomalies, load-test windows, startup spikes, and autoscaling artifacts from production telemetry
- Define and improve recommendation-quality measurement through regression testing, behavioral validation, and production accuracy and safety metrics
- Investigate and resolve recommendation-quality issues from customer environments by tracing data, preprocessing, and model behavior
- Serve as the team's machine learning authority and guide technical direction on ML questions and model-versus-heuristic tradeoffs
- Write production-grade Python for models and pipelines and share ownership of message consumption, metrics ingestion, and caching services
- Prototype and validate new optimization capabilities from research through gradual, feature-flagged rollout
- Stay current on time-series forecasting and resource optimization techniques and evaluate which are worth adopting
Requirements:
- Master's degree or higher in a quantitative field (Computer Science, Machine Learning, Statistics, Applied Mathematics)
- 5+ years of software engineering experience
- At least 3 years building and operating machine learning or statistical systems in production
- Expert-level Python, including typed, tested, production-grade code
- Fluency in numpy or similar array-based numerical computing
- Hands-on experience with time-series analysis and forecasting, including seasonality, trend decomposition, anomaly detection, and classical statistical methods
- Experience testing ML systems rigorously, including regression testing against known-good baselines, behavioral validation, and numerical reproducibility
- Working knowledge of Kubernetes, including resource requests and limits, autoscaling behavior, OOM kills, and CPU throttling
- Comfort owning a production service, including queues, caches, retries, observability, and debugging customer-environment issues from logs and metrics
- Clear written and verbal communication
- Experience with Prophet or similar forecasting libraries is beneficial
- Prometheus/PromQL and experience working with metrics at scale is beneficial
- Cloud cost optimization, capacity planning, or infrastructure efficiency background is beneficial
- AWS (S3, Managed Prometheus) experience is beneficial
- Experience being the ML domain expert on a team of generalists is beneficial
Benefits:
- 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
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