Observe is building a multi-tenant SaaS product in the Observability space. The core of the product is a novel data management platform that combines stream processing and a temporal-relational data model, implemented on top of a cloud data warehousing platform (Snowflake).
As a data-driven company, we track and collect statistics for each of the millions of queries we run each day. To fully harness our platform’s potential, we need to improve our query costing to be able to accurately predict resource demands and maximize our resource utilization and predict shifts in our workload and our underlying platform’s performance.
The ideal candidate for the Machine Learning Engineer role has a strong track record of applying machine learning techniques to solve real world problems and can quickly implement such models and prototype necessary data pipelines.
Ideal Candidate Profile
Masters or PhD in a relevant field, e.g., in Computer Science / Mathematics
5+ years of relevant software engineering experience
3+ years of hands-on Machine Learning experience with at least two of the following techniques: (a) learning stochastic processes such as hidden Markov models or Gaussian processes; (b) AI planning and/or reinforcement learning in Markov decision processes; (c) inference techniques such as Bayesian network; (d) ensemble learning techniques such as random forests
hands-on experience in neural networks is a plus, but not required
principles of relational data management or stream processing useful for context
We offer remote work possibilities for exceptional candidates.