Principal Machine Learning Engineer, SecureAI

United States Digital Space LLC; San Francisco (CA); On-site; USD 238,000 - 326,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

401(k)

Equity

Bonus

Job summary

Secures AI is hiring a Principal ML Engineer to advance its unified Identity Security Fabric for agent-based AI. You will replace static rules with dynamic AI security, delivering real-time threat inspection, intent evaluation, and behavioral analysis to keep autonomous agents within safe bounds.

You will architect features for a scalable control plane, aligning with the company’s mantra to get AI right by getting identity right, and you will mentor engineers while shaping security capabilities

Qualifications

• 10+ years of software development experience.
• Hands-on ML with feature engineering, training and fine-tuning models.
• Experience with Generative AI platforms (AWS Bedrock, OpenAI, etc.).
• Deep understanding of retrieval-augmented generation (RAG) and embeddings.

Responsibilities

• Implement intent-based enforcement to verify agent runtime requests.
• Apply LLM reasoning and prompt parsing in real time.
• Integrate low-latency inference engines into API gateway paths.
• Use embeddings and vector search to score alignment of intents and actions.
• Design confidence-scored decision engines feeding policy frameworks.
• Establish evaluation benchmarks and guardrails against bypasses.

Skills

Python

Go

Typescript

Machine learning

RAG

LangChain

FastAPI

PyTorch

TensorFlow

API design

Education

Bachelor’s or Master’s in CS

Tools

LangGraph

LlamaIndex

MCP

Airflow

Redis

Job description

**Secure Every Identity, from AI to Human

**Identity is the key to unlocking the potential of AI. the company secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence.

This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk.

About the company Secures AI Team

The the company for AI Agents Team is building the future of digital identity management. The way companies are using agents and allowing them access is undergoing a fundamental shift, enabling teams to move fast while staying secure.

Our North Star is clear: To get AI right, you have to get identity right. We are not just managing identities; we are building the industry's first Identity Security Fabric for the agentic era. As organizations rapidly deploy AI, these non-human entities are acting with access to critical tools, often bypassing traditional perimeters and creating a 'Shadow AI' crisis. Our mission is to move identity from a reactive gatekeeper to a unified control plane, transforming AI risk into business ROI.

We are tackling this by implementing a comprehensive four-pillar maturity model: Discover, Onboard, Protect, and Govern. We are driving innovation by defining the standards for Agentic Identity—including pioneer work with securing AI Agents and support for protocols like MCP. You will be helping us build the infrastructure that allows enterprises to scale AI safely, ensuring every access decision—whether made by a human or an automated agent is authenticated, authorized, and audited at scale.

We are a diverse team of engineers, product managers, and designers who are bringing the company’s expertise in identity to define the future of Agent Identity management, focusing on enablement while staying secure.

About the role

In this role, you will join the Agent Access Policies sub-team under the company Secures AI as a Principal Machine Learning (ML) Engineer to advance the company's authorization capabilities. By replacing static, rule-based systems with dynamic AI security mechanisms, you will deliver real-time threat inspection, agent intent evaluation, and behavioral analysis—ensuring autonomous AI agents operate safely within specified bounds.

Your core mission will be architecting and driving features for our unified control plane to establish the premier Identity Security Fabric for the agentic era. In doing so, you will help execute our guiding principle: "To get AI right, you have to get identity right."

What you’ll be doing

• Implement intent-based enforcement to verify agent runtime requests match their intended purpose, requiring key capabilities.

• Apply LLM reasoning and prompt parsing to interpret prompts, tool payloads, and intent in real time.

• Integrate low-latency inference or semantic evaluation engines directly into the API gateway request path.

• Use embeddings, vector search, or zero-shot classification to score alignment between agent intent and executed actions.

• Design confidence-scored decision engines that feed semantic verification results into policy frameworks (e.g., Cedar).

• Establish evaluation benchmarks, prompt injection defenses, and guardrails to prevent bypasses or false positives.

• Architect scalable ML and Generative AI systems integrating retrieval, inference, and evaluation pipelines.

• Optimize prompting, context retrieval, and RAG workflows for accuracy, safety, and efficiency in Claude-based systems.

• Build automated evaluation pipelines to measure model quality, correctness, groundedness, and safety in production.

• Implement schema validation, structured output enforcement and guardrails to ensure reliable, compliant AI outputs.

• Mentor and coach engineers to support team and community growth.

What you’ll bring to the role

• 10+ years of software development experience, with strong programming expertise in Python (and familiarity with Go or Typescript a plus).

• Hands-on experience with applied machine learning, from feature engineering to training and fine-tuning models.

• Hands-on experience with modern Generative AI platforms (AWS Bedrock, OpenAI, Anthropic, etc.).

• Deep understanding of retrieval-augmented generation (RAG), embeddings, and knowledge-base workflows.

• Hands-on experience with LiteLLM, LangGraph, LangChain, LlamaIndex, MCP, or other related AI agent frameworks.

• Familiarity with ML frameworks (FastAPI, PyTorch, TensorFlow, Spark ML) and workflow orchestration tools (Airflow, etc.).

• Experience defining evaluation metrics, pipelines, and feedback loops for ML/GenAI systems.

• Proven ability to collaborate with product and engineering teams to drive greenfield initiatives forward, navigate unknowns, and iterate quickly and frequently.

• Experience building tools or infrastructure for AI/ML applications, with a deep understanding of the developer lifecycle in an AI-native world.

Extra credit

• Experience integrating AI-driven systems with identity, authentication, or security products.

• Exposure to ethical AI, model risk, or compliance frameworks.

• Familiarity with evaluation datasets, synthetic data generation, or LLM-as-a-judge methods.

Education:Bachelor’s or Master's degree in Computer Science or related field

(P25822_3552624)

#LI-Hybird

#LI-BB1

Below is the annual base salary range for candidates located in San Francisco Bay Area. Your actual base salary will depend on factors such as your skills, qualifications, experience, and work location. In addition, the company offers equity (where applicable), bonus, and benefits, including health, dental and vision insurance, 401(k), flexible spending account, and paid leave (including PTO and parental leave) in accordance with our applicable plans and policies. To learn more about our Total Rewards program please visit: https://rewards.the company.com/us.

The annual base salary range for this position for candidates located in the San Francisco Bay area is between:

$238,000—$326,000 USD

The the company Experience

• Supporting Your Well-Being

• Driving Social Impact

• Developing Talent and Fostering Connection + Community

We are intentional about connection. Our global community, spa