Senior Machine Learning Engineer – Generative AI – Customer Identity
| Company | Okta |
|---|---|
| Location | San Francisco, CA, USA, Chicago, IL, USA, New York, NY, USA, Bellevue, WA, USA |
| Salary | $150000 – $253000 |
| Type | Full-Time |
| Degrees | Bachelor’s |
| Experience Level | Senior, Expert or higher |
Requirements
- Fluency in a computing language, e.g. Python, Scala, C++, Java, etc.
- Knowledge of AWS Bedrock, OpenAI or similar Generative AI platforms.
- Experience with developing production AI/ML systems and platforms at scale, including retrieval-augmented generation (RAG) and embedding workflows.
- Experience with LLMOps, CI/CD, and IaC.
- Familiar with the full AI/ML lifecycle from model development, training, testing, deployment, monitoring, and iterating.
- Knowledge in prompt engineering and guardrails.
- Excellent verbal and written communication.
- Exceptional troubleshooting and problem solving skills, thrive in a fast-paced, innovative environment.
Responsibilities
- Design and implement infrastructure and platform components for leveraging LLM in production.
- Tune and optimize LLM to improve response quality and optimize performance.
- Build workflows and pipelines to process data from myriad sources into a knowledge base for various use cases.
- Collaborate with platform engineering teams to ensure that AI/ML systems integrate successfully into production environments while adhering to performance and availability SLOs.
- Participate in project planning, design, development, and code reviews.
- Communicate verbally and in writing to business customers and leadership teams with various levels of technical knowledge, educating them about our systems, as well as sharing insights and recommendations.
- Partnership across Engineering, Product Management, Security and Design teams to solve technical and non-technical challenges.
Preferred Qualifications
- Bachelor’s degree in Computer Science, Engineering, or a related field.
- Familiar with training and fine tuning models at scale with experience in LLM evaluation.
- Experience with ML frameworks (e.g. TensorFlow, Spark ML, PyTorch), data workflow platforms (e.g. Airflow), and container technologies (e.g. Docker, Kubernetes).
- Familiar with Python and AI/ML libraries such as LangChain and FastAPI.
- Ability to work with ambiguity, ability to self-motivate, prioritizing needs, and delivering results in a dynamic environment.
- Combination of deep technical skills and business savvy to interface with all levels and disciplines within our and our customer’s organizations.