Remote] Machine Learning Engineer

As a Machine Learning Engineer, you should be proficient in building distributed systems, containerization, orchestration, and cloud-native architectures, you will collaborate with cross-functional teams to build scalable, reliable, and production-ready ML systems. Oracle is investing heavily in Generative AI, and as a ML Engineer, you must have a strong understanding and experience in leveraging the capabilities of Large Language Models. 

Your expertise will be pivotal in developing Conversational AI solutions and other cutting-edge Machine learning techniques to shape up and enhance users' experience with AI/ML/GenAI capabilities. You will also partner with product managers, software engineers, and operation teams to leverage engineering innovations to simplify the business requirements into scalable solutions.

The ideal candidate is highly technical, particularly around ML and AI, but can lead across the full stack, along with good product sense and business understanding, to map the technology choices to the context of each initiative. 

Disclaimer:

Certain U.S. based or U.S. customer or client-facing roles may be required to comply with applicable requirements, such as immunization/occupational health mandates, and/or drug testing requirements.

Range and benefit information provided in this posting are specific to the stated locations only

US: Hiring Range in USD from: $68,500 to $158,200 per annum. May be eligible for bonus and equity.

Oracle maintains broad salary ranges for its roles in order to account for variations in knowledge, skills, experience, market conditions and locations, as well as reflect Oracle's differing products, industries and lines of business.
Candidates are typically placed into the range based on the preceding factors as well as internal peer equity.

Oracle US offers a comprehensive benefits package which includes the following:
1. Medical, dental, and vision insurance, including expert medical opinion
2. Short term disability and long term disability
3. Life insurance and AD&D
4. Supplemental life insurance (Employee/Spouse/Child)
5. Health care and dependent care Flexible Spending Accounts
6. Pre-tax commuter and parking benefits
7. 401(k) Savings and Investment Plan with company match
8. Paid time off: Flexible Vacation is provided to all eligible employees assigned to a salaried (non-overtime eligible) position. Accrued Vacation is provided to all other employees eligible for vacation benefits. For employees working at least 35 hours per week, the vacation accrual rate is 13 days annually for the first three years of employment and 18 days annually for subsequent years of employment. Vacation accrual is prorated for employees working between 20 and 34 hours per week. Employees working fewer than 20 hours per week are not eligible for vacation.
9. 11 paid holidays
10. Paid sick leave: 72 hours of paid sick leave upon date of hire. Refreshes each calendar year. Unused balance will carry over each year up to a maximum cap of 112 hours.
11. Paid parental leave
12. Adoption assistance
13. Employee Stock Purchase Plan
14. Financial planning and group legal
15. Voluntary benefits including auto, homeowner and pet insurance

The role will generally accept applications for at least three calendar days from the posting date or as long as the job remains posted.

Career Level - IC2

• Proficient in creating detailed design specifications and writing elegant code.Familiarity with Python, and ML frameworks such as PyTorch, TensorFlow, etc. is a must
• Lead the incubation of new initiatives, architect scalable solutions, and drive strategic technology choices to develop and deliver AI/ML capabilities for our customers.
• Develop high-quality, scalable, and efficient software solutions that integrate generative AI capabilities.
• Design, develop, test, and deploy Machine learning models, including large-language models and build pipelines at scale for batch and real-time use cases.
• Leverage third-party and in-house ML tools & OCI platform to develop reusable, highly performant machine learning systems with low-latency serving and reliable means to update/re-train ML models.
• Work collaboratively with cross-functional partners including product managers, operations, and data scientists, identify opportunities for business impact, understand and prioritize requirements for machine learning systems and data pipelines, drive engineering decisions, and quantify impact.
• Communicate continually with the project teams, and explain progress on the development effort.
• Ensures quality of work through development standards and QA procedures.
• Keen on improvising technical solutions and processes emphasizing discovery through doing, iteration, and feedback loops.