Senior Machine Learning Engineer

Preferred Skills:

Experience collaborating with data science teams and understanding their needs/challenges
Ability to lead initiatives and communicate effectively with technical teams and senior leadership
Proven ability to understand business problems and identify technical solutions
Familiarity with ML tools and frameworks, including cloud-based ML Ops
Retail experience is a plus

Top Priorities:

Focus on engineering-first candidates with a strong ML interest
MLOps background (NOT model development or deployment experience)
Titles like "Machine Learning Engineer" are ideal, emphasizing responsibility for the entire ML workflow

Team Dynamics:

Strong communication skills are essential to work effectively with a team that has strong opinions
Expected to lead and drive collaboration

Typical Duties

Maintain expertise in a range of ML technologies and platforms, with a preference for Google Vertex AI, while remaining open to other systems as needed
Leverage open-source frameworks like TensorFlow, PyTorch, and scikit-learn, integrating them with ML frameworks via custom containers
Stay updated on the latest trends in MLOps and ML technologies
Apply hands-on experience to design and develop recommender systems using ML techniques such as embedding-based retrieval, reinforcement learning, transformers, and LLMs
Utilize software engineering skills to integrate recommender systems into customer-facing products
Conduct A/B testing and iterative optimization using data-driven approaches
Ensure an understanding of the infrastructure needs for deploying ML systems, including CPU/GPU and networking infrastructure
Manage, share, and reuse machine learning features at scale using Vertex AI Feature Store
Implement feature stores as central repositories to enhance transparency in ML operations across the organization
Enable secure feature delivery through endpoint exposure while maintaining authority and security features
Assist with data labeling and management to ensure high-quality data for ML models
Collaborate with data engineers and scientists to ensure data integrity and efficiency in ML model development
Support end-to-end integration from data to AI, leveraging tools like BigTable and BigQuery to execute ML models on business intelligence tools
Monitor ML systems in production, identify improvement opportunities, and implement optimizations
Participate in support rotations and handle support calls as necessary

Requirements

Proven experience working collaboratively with data science teams, addressing their needs and challenges
Strong engineering skills with a focus on MLOps, ensuring effective management of the entire ML workflow
Expertise in Google Cloud Platform (GCP) and its services for machine learning operations
Ability to lead initiatives and effectively communicate with both technical teams and senior leadership
Proven ability to understand complex business problems and identify practical technical solutions
Familiarity with a range of ML tools and frameworks, with an openness to adopting emerging technologies

Nice to Have

Retail industry experience
Strong communication skills to foster collaboration and drive initiatives across teams
Demonstrated ability to take ownership of ML operations in a fast-paced, operational role

Job Type: Contract
Pay: $68.00 - $75.00 per hour
Benefits:

401(k)
401(k) matching
Dental insurance
Health insurance
Life insurance
Paid time off
Vision insurance

Compensation Package:

Hourly pay

Schedule:

8 hour shift
Monday to Friday

Application Question(s):

Please mention whether you have a VISA, Green Card or are a US Citizen?

Education:

Bachelor's (Required)

Experience:

Python: 6 years (Required)
MLOps: 5 years (Required)
Infrastructure Management: 5 years (Required)
Data Pipeline Automation: 3 years (Required)

Work Location: Remote