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: Washington D.C, USA - Hybrid (2-3 days/week)
Salary: $134k- $181k + bonus eligibility and equity
FULL-TIME
What you'll do:
- Work with the Data team and Data Science to deliver features to support dataset curation, model analysis, and integrations that span the entire machine learning lifecycle
- Build powerful and extensible methods that interact with our event-driven and real-time data pipelines, allowing for high throughput and resilient model serving.
- Help build, design, and monitor production models for errors, drift, and non-compliance.
- Work with our SRE team to build, deploy, and manage the necessary platform and cloud infrastructure to support the machine learning life-cycle.
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 call.
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*This role requires you to be based in the US*
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