Sr. AI/ML Engineer

Vectra • San Jose, CA

60d • $165,000 - $200,000 • Hybrid

About The Position

We’re hiring an AI/ML Engineer to design, build, and deploy machine learning systems at the heart of our platform. You’ll work on problems like dynamic threat detection, LLM-powered agent reasoning, RAG pipelines, and adversarial behavior modeling. You’ll collaborate across research, backend, and security teams to ship high-impact features fast.
If you're a hands-on ML engineer who loves shipping smart systems in messy real-world domains, this is for you.

Requirements

• 3+ years of hands-on experience in applied ML or MLOps (or PhD with practical implementation).
• Strong ML fundamentals (classification, clustering, anomaly detection, embedding techniques).
• Experience working with real-world noisy data, ideally in time series, logs, or graph-structured form.
• Experience with LLMs (e.g., OpenAI, Mistral, Llama), embeddings, and vector databases. Well-versed in techniques for long context (chunking, sliding window, hierarchical summarization).
• Solid skills in Python, PyTorch or TensorFlow, and ML pipelines.
• Strong collaboration and ownership mindset—you write clean code, ask great questions, and iterate quickly.
• Someone who is looking for a hybrid work environment, with a minimum of 3 days in the San Jose office.

Nice To Haves

• Familiarity with cybersecurity data (SIEM logs, alerts, EDR telemetry, threat reports).
• Experience with agent frameworks (LangChain, AutoGen, CrewAI) and agent protocols.
• Exposure to adversarial machine learning or explainability techniques.
• Infra skills for ML (Docker, K8s, GPU scheduling, model serving).

Responsibilities

• Build and fine-tune models for threat detection, anomaly detection, and behavioral modeling (supervised, unsupervised, and semi-supervised).
• Implement and optimize LLM-powered agents that reason over structured and unstructured security data.
• Develop RAG pipelines that combine embeddings, vector search, and context injection.
• Work with streaming and historical security data (logs, events, alerts) to train and evaluate models.
• Collaborate with backend and platform teams to deploy models in scalable, low-latency environments.
• Continuously improve model performance, robustness, and explainability.

Benefits

• Founding-level equity and full-stack ownership.
• Work on hard, meaningful problems at the intersection of AI and security.
• High agency, fast learning, direct access to customers and users.
• Backed by seasoned operators and security leaders.
• Compensation includes competitive base pay, incentive plan eligibility, and participation in the employee equity plan (stock options).
• Specific benefits offered varies by location, but commonly include health care insurance, income protection / life insurance, access to retirement savings plans, behavioral & emotional wellness services, generous time away from work, and a comprehensive employee recognition program.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 501-1,000 employees