Machine Learning Engineer at Bestenouf Consulting

About the job

MeeBoss is sharing this role on behalf of Bestenouf Consulting. This role involves taking direct ownership of a core recommendation stack, improving experimentation velocity, and driving metrics from offline modeling through online A/B results.

The position focuses on evolving large-scale recommendation systems and applying AI and LLM technologies to real-world production problems to enhance content discovery, personalization, and developer productivity.

Job title

Machine Learning Engineer

Location

San Francisco, CA (On-site)

Why this role

• Take direct ownership of core recommendation systems serving tens of millions of users.
• Drive AI-driven innovation projects and improve content discovery and personalization.
• Work on 0 to 1 AI-powered projects, taking ideas from early exploration through MVP to production.

What you will do

• Design, build, and iterate on high throughput, low latency recommendation systems and power core feeds.
• Improve retrieval, ranking, and multi-objective optimization to balance engagement, retention, content quality, and business metrics.
• Own systems from offline training through online inference, A/B experimentation, and metric analysis.
• Identify and resolve issues across data quality, model drift, and system performance in production.
• Apply LLMs and foundation models to recommendation problems, solving content understanding, user intent modeling, and feature generation challenges.
• Prototype quickly, validate through experiments, and push successful ideas into production.
• Embrace AI-assisted development workflows and apply LLM-powered coding, debugging, and analysis across feature ideation, prototyping, and experiment analysis.

What we are looking for

• 3 to 6 years of production ML and recommendation systems experience.
• Deep hands-on production recommendation systems experience, including shipping and tuning ranking, retrieval, and embeddings at scale.
• End-to-end experiment ownership, delivering offline evaluation through online A/B testing to results analysis.
• Strong understanding of recommendation system fundamentals across ranking, embeddings, user and item modeling, and experimentation.
• Strong coding skills in Python, Java, or Scala, with experience building with PyTorch or TensorFlow.
• Experience working with large-scale data and ML systems, including Spark, distributed training, and real-time inference in production.
• Ability to own features end-to-end and deliver measurable business impact.
• Must work from the Bay Area, Mountain View, California.
• Qualify for sponsorship of new work authorization, including H-1B lottery candidates and OPT holders.

Benefits

• Health, dental, and vision care for you and your family, with 100 percent coverage for the employee.
• Top-tier 401(K) plan with company matching.
• Paid time off and paid holidays.
• FSA, HSA, and commuter benefits programs.
• Team activity budget.

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