ent-security - ML Ops/Production Engineer
Requirements
• A track record of owning and designing whole systems end to end, shown by growing scope across roles or companies. • Experience training, evaluating and deploying your own models in production. Integrating LLM APIs alone is not enough for this role. • The ability to take someone else's algorithm, understand the math and logic behind it, and turn it into correct, tested production code. • A strong grasp of model evaluation: designing evals beyond simple tests, reasoning about regressions, and knowing how you'd tell that a model was ready to ship. • Strong engineering skills in Python and at least one systems or backend language (C/C++, Java, Rust or Go). • Curiosity beyond your own job description, including about where AI and security are heading. • Comfort with ambiguity: you define the problem, then solve it, and you close gaps without being asked. • Edge or resource-constrained inference (ONNX Runtime, llama.cpp, Core ML, TensorRT, OpenVINO or similar), with results you can quantify. • Building and operating data or inference pipelines at scale, with a solid grasp of cost and latency tradeoffs. • Building infrastructure without a big-company platform behind you, at a startup or with your own setup. • Using AI coding agents as a core part of your workflow, with judgment about checking what they produce. • Production LLM or agentic systems, including evaluating probabilistic outputs, agent harnesses and MCP. • Background in security: endpoint detection, DLP, behavioral analytics, or protecting AI agents.
Responsibilities
• Take models and algorithms from our scientists into production, understanding them well enough to reimplement them correctly (for example Python to C++ or Java) and verify parity with the original. • Own models after launch, including retraining, monitoring and evaluation, so researchers can move on to the next problem. • Design evaluation and release gates that decide when a model is safe to ship, catch regressions from model or data changes, and explain shifts in metrics such as precision and recall. • Build production monitoring for model quality, including drift detection, retrain triggers and ground-truth strategies for unlabeled data. • Run inference within tight latency, memory, CPU and power budgets on the endpoint, and at scale in the cloud, using quantization, distillation, batching and caching. • Build the pipelines that connect the two layers, so behavioral baselines and context learned in the cloud flow reliably back to endpoints. • Build the infrastructure for training and experimentation (datasets, reproducible training, CI/CD for models) and keep it simple enough for a fast-moving team.
Benefits
• Distributed workplace. While we have positions we hire for in our SF office, we also hire remotely across North America. • Own a piece of the journey. Every teammate gets meaningful equity on top of their salary. • We’ve got you covered. Generally, we pay 90% of your medical, dental, and vision is paid by Ent. We also cover 75% for your dependents. • Take the time you need. Our flexible PTO lets you recharge, travel, or just take a breather. • Family matters. 14 weeks of fully paid parental leave (birth, adoption, or foster)! • Live well. A $100 monthly lifestyle account to spend on what keeps you healthy and happy — fitness, wellness, learning, and more. • Set up your space. A $500 home office stipend when you join as a remote employee. • DIVERSITY & ACCOMMODATIONS • We’re committed to building a diverse, inclusive, and equitable workplace where people of all backgrounds, identities, experiences, and abilities are welcomed, valued, and supported. We recognize there is no single path to success and value nontraditional career journeys and diverse perspectives as key to building stronger, more innovative teams.
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