[Hiring] Employees Machine Studying Engineer @Irregular

In regards to the Function

Irregular Safety is in search of a Employees Machine Studying Engineer to affix the Message Detection – Assault Detection staff.  At Irregular, we shield our clients in opposition to nefarious adversaries who’re consistently evolving their strategies and ways to outwit and undermine the standard approaches to Safety. That’s what makes our novel behavioral-based method so…Irregular. Irregular has consistently been named as one of many top cybersecurity startups and our behavioral AI system has helped us win numerous cybersecurity accolades leading to being trusted to guard greater than 17% of the Fortune 1000 ( and ever rising ).

In a panorama the place a single profitable assault can result in monetary losses of thousands and thousands of {dollars}, the Assault Detection staff performs the central position of constructing an especially excessive recall Detection Engine that may function on a whole lot of thousands and thousands of messages at milliseconds latency. The Assault Detection staff’s mission assertion is to supply world-class detector efficacy to deal with altering assault panorama utilizing a mix of generalizable and auto educated fashions in addition to particular detectors for top worth assault classes.

This position is central to our mission of defending the world’s largest enterprises. You may be accountable for reasoning concerning the gaps in our multi-layered detection system and proposing generalizable ML options. You should have a big impression on our technical roadmap, guiding how our various set of detection fashions—spanning behavioral evaluation, pure language understanding, and deep studying methods—work in live performance. This can be a distinctive alternative to form the way forward for our ML structure, from evolving our core coaching and deployment methods for a worldwide infrastructure to defining how our core ML capabilities will be uncovered as scalable providers to energy different merchandise throughout the corporate.

What you’ll do

• Function a technical chief and subject material skilled, offering architectural steering and mentorship throughout a number of machine studying workstreams.
• Architect and design generalizable ML methods to handle probably the most essential gaps in our detection capabilities, shifting past incremental enhancements.
• Motive holistically about our whole detection engine, defining the architectural imaginative and prescient for a way totally different courses of fashions—from heuristic and behavioral to complicated deep studying methods—ought to combine and function.
• Drive the technical roadmap for foundational, long-term tasks, akin to evolving our world mannequin coaching paradigms and creating centralized ML capabilities that may be leveraged as platforms by different groups.
• Present technical mentorship and suggestions on ML choices throughout totally different workstreams, elevating the efficiency of all the staff.
• Personal the end-to-end ML lifecycle: from knowledge evaluation, function engineering, and mannequin prototyping to working with infrastructure groups on productionization, deployment, and monitoring of large-scale fashions.
• Examine complicated mannequin efficiency points, making use of a deep theoretical understanding of machine studying and deep studying to diagnose and resolve them.
• Constantly adapt our methods to new, unseen assaults by growing and refining our automated mannequin retraining and analysis pipelines.

Should Haves

• 8+ years of expertise designing and constructing high-impact, customer-facing machine studying purposes.
• Confirmed expertise engaged on ML at scale with direct product impression in mature ML industries akin to suggestion methods, advert tech, quantitative finance, or fraud detection.
• Sturdy grasp of the theoretical limitations of deep studying fashions and a scientific method to investigating and debugging poor mannequin efficiency.
• Demonstrated expertise within the productionization of large-scale ML fashions in fast-feedback environments.
• Means to motive about summary system gaps and suggest generalizable, architecturally sound ML options, not simply level fixes.
• Experience throughout all the ML lifecycle, from knowledge exploration and have engineering to mannequin deployment and on-line scoring.
• Fluency in Python and ML frameworks like Scikit-learn, PyTorch, or TensorFlow.
• BS diploma in Laptop Science, Utilized Sciences, Info Programs, or a associated engineering area.

Good to Haves

• MS or PhD diploma in Laptop Science, Electrical Engineering, or one other associated engineering/utilized sciences area.
• Expertise main multi-quarter, cross-functional ML tasks.
• Expertise with MLOps instruments and constructing scalable knowledge pipelines.

This place isn’t: 

• A research-oriented position that is two-steps faraway from the product or buyer

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