Software Development Engineer (Machine Learning)

Fortinet
Sunnyvale
Workplace: HybridFull timeUSD 150,000 - 183,000 annuallyFunction: Data Science & Machine LearningEducation: bachelorsSkills: ["Evaluation","Communication","Collaboration","Agile delivery","Precision/recall reasoning"]

Build and train guardrail and threat-detection models that spot prompt injection, jailbreaks, unsafe content, and sensitive data exposure with strict latency constraints. Design tiered detection cascades and fine-tune encoder/decoder model components, then optimize and deploy models inline using quantization, distillation, and GPU inference stacks. Evaluate detection performance under realistic false-positive rates and harden systems against evolving evasion and multi-turn attacks.

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FursaFursa
Fortinet
Fortinet
3 days ago

Software Development Engineer (Machine Learning)

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Last checked: 4 hours agoStatus: Live

Job Summary

Build and train guardrail and threat-detection models that spot prompt injection, jailbreaks, unsafe content, and sensitive data exposure with strict latency constraints. Design tiered detection cascades and fine-tune encoder/decoder model components, then optimize and deploy models inline using quantization, distillation, and GPU inference stacks. Evaluate detection performance under realistic false-positive rates and harden systems against evolving evasion and multi-turn attacks.
Location: Sunnyvale
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning

Key Responsibilities

  • •Build and train guardrail and detection models for prompt injection, jailbreak attempts, unsafe content, and sensitive data exposure.
  • •Design and tune a tiered detection cascade to meet accuracy targets within a fixed per-request latency budget.
  • •Fine-tune encoder-based classifiers/token-level taggers and adapt decoder models for semantic judgment, using distillation to enable deployment-sized models.
  • •Optimize and serve models inline by quantizing, distilling, compiling (ONNX Runtime, TensorRT, INT8/FP8), and deploying on Triton and vLLM with batching and memory/KV-cache tuning.
  • •Harden models against evasion by researching attacks, converting new bypasses into training data and regression tests, and monitoring evaluation drift with benchmarks and suites.

Pay and Benefits

Salary: USD 150,000 - 183,000 annually
Perks:Health InsuranceDentalVisionLife InsuranceDisability Insurance401kPaid HolidaysPaid Leave

Key Requirements

  • •Strong Python and production PyTorch experience, with comfort in performance-critical languages (Go/Rust/C/C++).
  • •Experience training and fine-tuning transformer models using Hugging Face Transformers or equivalent.
  • •Production experience with modern inference serving systems (Triton, vLLM, TensorRT-LLM, TGI), including batching and memory tuning for throughput.
  • •Practical model optimization experience such as quantization, distillation, pruning, or graph compilation while maintaining accuracy.
  • •Ability to design deployment-realistic evaluation test sets and reason about precision/recall to avoid blocking legitimate traffic.
Experience:Machine learningLLM securityAdversarial ML
Education:Bachelor's
Skills:EvaluationCommunicationCollaborationAgile deliveryPrecision/recall reasoning
Tech Stack:PythonPyTorchGoRustCC++Hugging Face TransformersTriton Inference ServerVLLMTensorRT-LLMTGIONNX RuntimeTensorRTINT8FP8DockerKubernetesUnicode

Eligibility

Work Authorization:Authorization required. Sponsorship not provided.

Company Brief

Fortinet
Provides enterprise cybersecurity solutions including data protection, cloud security, network security, and user and entity behavior analytics to help organizations prevent insider threats, secure cloud and on-premises environments, and enforce data loss prevention policies.
Industry: Cybersecurity
Company Size: Enterprise (1,001+ employees)
Growth: Established Company
Funding: Private Equity Backed
Headquarters: Austin, United States
Founded: 1994
WebsiteLinkedIn