Machine Learning Engineer
Listed on 2026-11-01
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Software Development
Machine Learning/ ML Engineer, AI Engineer (Applied/Software)
Location: Remote, Americas (UTC-8 to UTC-3) or Onsite in San Francisco, CA
Employment Type: Full-Time
Compensation: $170,000–$220,000 Base Salary + Equity
Industry: AI Infrastructure / Cybersecurity / Agentic AI
About the OpportunityShields Group Search is partnering with an innovative AI infrastructure and security startup to hire a Machine Learning Engineer
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Build the models that tell a trusted agent from an attack, in real time and under load.
Our client verifies AI agents in real time, on the request path. That means the models behind it run under the same latency budget as the rest of production.
There is no offline batch job to hide behind, and no second chance if a call is slow or wrong.
Work Arrangement: You can work remotely from anywhere in the Americas, between UTC-8 and UTC-3, or on-site from the company's San Francisco office, whichever you prefer. The company is not hiring outside that time-zone band for this role.
What We're Looking For Must-Haves- Years of production ML experience, building, evaluating, and deploying models, not just research or notebooks.
- Strong Python and a modern ML framework (PyTorch or Tensor Flow); solid SQL.
- Production ML systems end to end: feature pipelines, training, evaluation, and low-latency, real-time serving.
- Comfort with low-label and cold-start problems, including anomaly detection, unsupervised methods, weak supervision, and building evaluation sets and labels from scratch.
- Data analysis, statistics, and experiment design.
- Strong software engineering skills. You ship versioned, testable, reproducible code, not throwaway models.
- Applied ML experience in fraud, risk, abuse, account integrity, or security.
- Graph ML / GNNs, sequence or behavioral modeling, or anomaly detection.
- LLM / NLP work, and familiarity with AI agents, including tool use, function calling, and MCP.
- Experience in adversarial settings, where the threat evolves against the model.
- Explainable, auditable, reproducible ML, with real model versioning.
- Experience with payment protocols (x402), agent identity (DID / VC), or crypto commitments (Merkle anchoring).
- Equity: Included
- Location: Remote across the Americas (UTC-8 to UTC-3) or onsite in San Francisco
- Employment: Full-Time
Interested in building production ML systems at the intersection of AI agents, security, and real-time infrastructure?
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