hackajob has partnered with a company provide the most accurate internet intelligence data available to uncover risks and take down threats at scale.
Role : Machine Learning Engineer
Location : Los Altos, CA - Hybrid (2-3 days / week)
Salary : $134k- $181k + bonus eligibility and equity
FULL-TIME
Job Responsibilities
- Collaborate with the Data and Data Science teams to develop features that support dataset curation, model analysis, and integration across the entire machine learning lifecycle.
- Build scalable, extensible methods that interact with event-driven and real-time data pipelines, enabling high-throughput and resilient model serving.
- Help build, design, and monitor production models for errors, drift, and non-compliance.
- Partner with the Site Reliability Engineering (SRE) team to build, deploy, and manage cloud infrastructure that supports the machine learning lifecycle.
Qualifications :
- Bachelor's degree in Computer Science, Data Science, Engineering, or other technical discipline (or equivalent professional experience).
- 5+ years of experience working as a Machine Learning Engineer, MLOps Engineer, or Data Engineer.
- Proficiency with building production-grade models using Machine Learning and Deep Learning frameworks (e.g., TensorFlow or PyTorch ).
Including experience with model training and drift detection.
- Proficiency with backend software development (e.g., Python , Golang ) with experience in service-oriented architectures based on Kubernetes , or similar.
- Familiarity with or experience integrating into real-time, event-driven, and streaming data pipelines, preferably those built on Google Pub / Sub, Kafka , or similar.
- Preferred familiarity GCP VertexAI cloud platform.
- Preferred familiarity with Google BigQuery and its ecosystem.
- Ability to concisely communicate complex subject matter to technical and non-technical audiences.
If you're interested in finding out more about this fantastic opportunity please get your application in and we can arrange a ca ll.
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- This role requires you to be based in the US*
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