Staff Machine Learning Engineer, Home Surfaces

Ladders; United States; Remote; USD 227,000 - 325,000; Full time

Benefits offered by this job

Health insurance coverage

Six months of paid parental leave

401(k) retirement plan

Monthly meal allowance

23 paid days off annually

13 paid flexible holidays

Paid sick leave

Job summary

Ladders is seeking a Staff Machine Learning Engineer, Home Surfaces to shape ML systems powering personalized content discovery and user experiences at scale. You will own core recommendation and ranking capabilities for a high-traffic consumer product with a focus on relevance, experimentation, and performance.

Join a remote US-based team to train and optimize large language models, improve the ML platform, and collaborate with cross-functional partners to drive experimentation and production

Qualifications

• 5–7 years of production ML experience.
• Expertise in recommendation/ranking for large-scale content discovery platforms.
• Proficiency in Python with PyTorch.
• Experience with large language model training and optimization techniques (SFT, distillation).
• Familiarity with latency and cost optimization in large-scale inference.

Responsibilities

• Own and enhance ML models for home feed and shortcut experiences.
• Design and implement personalized recommendations for millions of users globally.
• Develop content recommendation systems for AI-powered user experiences.
• Train and optimize large language models using advanced techniques.
• Collaborate with cross-functional teams to define and execute experimentation strategies.
• Conduct A/B testing and continuous optimization of recommendation systems.
• Enhance ML platform capabilities and production systems for personalization.

Skills

Recommender systems

Large-scale content discovery

Python

A/B testing

ML platform

Cross-functional collaboration

Latency optimization

Tools

PyTorch

Job description

For our client, we are seeking a Staff Machine Learning Engineer, Home Surfaces to join the team of a leader in the Media space.

This role will shape the machine learning systems that power personalized content discovery and user experiences at scale. You will own core recommendation and ranking capabilities for a high-traffic consumer product, with a strong focus on relevance, experimentation, and performance. The position also involves advancing AI-enabled experiences by training and optimizing large language models and improving the underlying ML platform.

Location: Remote - US based candidates only, no visa sponsorship available

Compensation: $227,495 – $324,993 annually

Responsibilities

• Own and enhance machine learning models for the company’s home feed and shortcut experiences
• Design and implement personalized recommendations for millions of users globally
• Develop content recommendation systems for AI-powered user experiences
• Train and optimize large language models using advanced techniques
• Collaborate with cross-functional teams to define and execute experimentation strategies
• Conduct A/B testing and continuous optimization of recommendation systems
• Enhance ML platform capabilities and production systems for personalization

Qualifications

• 5–7 years of experience building and deploying machine learning systems in production environments
• Expertise in recommendation systems, ranking models, and large-scale content discovery platforms
• Proficiency in Python with hands-on experience using PyTorch for machine learning
• Experience with large language model training and optimization techniques such as SFT and distillation
• Familiarity with large-scale inference systems, with a focus on latency and cost optimization
• Strong communication skills for collaborating with both technical and non-technical stakeholders
• Experience with distributed machine learning workloads and data pipeline orchestration

Benefits

• Health insurance coverage
• Six months of paid parental leave
• 401(k) retirement plan
• Monthly meal allowance
• 23 paid days off annually
• 13 paid flexible holidays
• Paid sick leave

Our client is an equal opportunity employer.