Staff Machine Learning Engineer, SecureAI
Okta is hiring a Staff Machine Learning Engineer, SecureAI in Toronto, Canada. It pays CAD 168k-CAD 231k a year and Level rates it ; you can apply on Okta.
Level's read on this job
- Builds AI ●●●● is how Level rates this machine learning engineer role: the job is building AI systems.
- The posting mentions OpenAI, Claude and Anthropic among its tools and skills.
- Level classes it as a senior role.
- Okta has 385 other open roles on Level.
AI in this role
Architect dynamic AI security mechanisms for real-time threat inspection and agent intent evaluation.
Secure Every Identity, from AI to Human
Identity is the key to unlocking the potential of AI. Okta 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 Okta Secures AI Team
The Okta 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 Okta’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 within Okta Secures AI as a Staff Machine Learning (ML) Engineer to advance Okta'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.
What you’ll bring to the role
- 8+ 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.
- Education:Bachelor’s or Master's degree in Computer Science or related field
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.
(P25833_3552618)
#LI-Hybrid
#LI-BB1
Below is the annual salary range for candidates located in Canada. Your actual salary will depend on factors such as your skills, qualifications, and experience. In addition, Okta offers equity (where applicable), bonus, and benefits, including health, dental, and vision insurance, RRSP with a match, healthcare spending, telemedicine, 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.okta.com/can.
The annual base salary range for this position for candidates located in Canada is between:$168,000—$231,000 CADThe Okta Experience
- Supporting Your Well-Being
- Driving Social Impact
- Developing Talent and Fostering Connection + Community
We are intentional about connection. Our global community, spanning over 20 offices worldwide, is united by a drive to innovate. Your journey begins with an immersive, in-person onboarding experience designed to accelerate your impact and connect you to our mission and team from day one.
Okta is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, marital status, age, physical or mental disability, or status as a protected veteran. We also consider for employment qualified applicants with arrest and convictions records, consistent with applicable laws.
If reasonable accommodation is needed to complete any part of the job application, interview process, or onboarding please use this Form to request an accommodation.
Notice for New York City Applicants & Employees: Okta may use Automated Employment Decision Tools (AEDT), as defined by New York City Local Law 144, that use artificial intelligence, machine learning, or other automated processes to assist in our recruitment and hiring process. In accordance with NYC Local Law 144, if you are an applicant or employee residing in New York City, please click here to view our full NYC AEDT Notice.
How we rate this
Staff Machine Learning Engineer, SecureAI at Okta rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in machine learning engineer jobs at this level
Pay
- Typical machine learning engineer job
- $200k
Use the typical machine learning engineer figure as a reference when pay comes up. See machine learning engineer jobs
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where ai security was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: RAG, AI agents, Fine-tuning, Machine learning, and AI Security. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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