Abnormal AI is looking to solve the problem of protecting customers against evolving cyber threats by building a high recall Detection Engine that can operate on hundreds of millions of messages at milliseconds latency.
Requirements
- 8+ years of experience designing and building high-impact, customer-facing machine learning applications.
- Proven experience working on ML at scale with direct product impact in mature ML industries such as recommendation systems, ad tech, quantitative finance, or fraud detection.
- Strong grasp of the theoretical limitations of deep learning models and a systematic approach to investigating and debugging poor model performance.
- Demonstrated experience in the productionization of large-scale ML models in fast-feedback environments.
- Ability to reason about abstract system gaps and propose generalizable, architecturally sound ML solutions, not just point fixes.
- Expertise across the entire ML lifecycle, from data exploration and feature engineering to model deployment and online scoring.
- Fluency in Python and ML frameworks like Scikit-learn, PyTorch, or TensorFlow.
Responsibilities
- Serve as a technical leader and subject matter expert, providing architectural guidance and mentorship across multiple machine learning workstreams.
- Architect and design generalizable ML systems to address the most critical gaps in our detection capabilities, moving beyond incremental improvements.
- Reason holistically about our entire detection engine, defining the architectural vision for how different classes of models—from heuristic and behavioral to complex deep learning systems—should integrate and operate.
- Drive the technical roadmap for foundational, long-term projects, such as evolving our global model training paradigms and creating centralized ML capabilities that can be leveraged as platforms by other teams.
- Own the end-to-end ML lifecycle: from data analysis, feature engineering, and model prototyping to working with infrastructure teams on productionization, deployment, and monitoring of large-scale models.
- Investigate complex model performance issues, applying a deep theoretical understanding of machine learning and deep learning to diagnose and resolve them.
- Continuously adapt our systems to new, unseen attacks by developing and refining our automated model retraining and evaluation pipelines.
Other
- BS degree in Computer Science, Applied Sciences, Information Systems, or a related engineering field.
- MS or PhD degree in Computer Science, Electrical Engineering, or another related engineering/applied sciences field (nice to have).
- Experience leading multi-quarter, cross-functional ML projects (nice to have).
- Base salary range: $229,500—$270,000 USD
- Eligible for a bonus, restricted stock units (RSUs), and benefits.
