Lead, mentor, and empower a team of Machine Learning Engineers, ensuring ownership of model quality throughout the entire lifecycle, from experimentation to production-ready deployment. Conduct regular 1-on-1s, support career development, provide constructive feedback, remove blockers, and promote team well-being. Drive team execution through prioritization, sprint planning, and resource allocation to ensure predictable, high-quality delivery. Act as the primary technical representative for the team, collaborating closely with Data Science, Engineering, and Product stakeholders. Own the technical roadmap for machine learning initiatives, prioritizing improvements based on measurable performance gains and business impact. Guide the design, training, fine-tuning, and evaluation of machine learning models across multiple domains, including classification, similarity search, reputation scoring, computer vision, and threat detection. Define model quality standards, evaluation frameworks, benchmarking processes, and release criteria. Establish robust model delivery standards, including versioning, portability, performance validation, and documentation requirements. Ensure production readiness across a variety of deployment environments, including cloud, offline, air-gapped, and embedded systems.
- Lead and mentor a team of Machine Learning Engineers, supporting performance, growth, and day-to-day execution.
- Conduct 1-on-1s, provide feedback, remove blockers, and foster a high-performing team culture.
- Own sprint planning, prioritization, resource allocation, and delivery management.
- Act as the primary technical liaison between ML, Engineering, Product, and Data Science teams.
- Define and drive the machine learning roadmap based on business impact and model performance.
- Guide the development, training, fine-tuning, and evaluation of ML models across multiple domains.
- Establish model quality metrics, evaluation frameworks, and release acceptance criteria.
- Ensure model artifacts are properly versioned, documented, portable, and production-ready.
- Oversee deployment readiness across cloud, offline, air-gapped, and embedded environments.
- Review technical solutions, model performance, and architectural decisions to ensure scalable and reliable ML systems.
What We're Looking For
- Proven experience leading or managing technical teams, including performance management, mentoring, and day-to-day work organization.
- At least 3 years of hands-on machine learning experience in two or more of the following areas:
- Text or content classification
- Computer Vision
- Similarity Search and Embedding Models
- Malware Detection
- Threat Detection
- Strong Python skills and experience with machine learning frameworks such as PyTorch or TensorFlow.
- Deep understanding of model evaluation methodologies and experimental design.
- Proven track record of delivering machine learning models that are successfully consumed by engineering teams in production.
- Strong foundation in statistics and rigorous experimentation.
Nice to Have
- Experience in cybersecurity-focused machine learning, including malware classification, phishing detection, or URL reputation systems.
- Experience with model compression, optimization, quantization, or deployment in resource-constrained environments.
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