This role pays more than
88%
of similar roles. Most pay
$177,537–$249,750
— the shaded band above.
At the midpoint, this role pays about
$286k
versus about
$214k
for comparable roles.
Based on 240 similar postings.
Employer
About Snap Inc.
Snap Inc. is a technology and camera company, best known for Snapchat, offering visual communication, augmented reality, and advertising products.
Snap Inc. currently has
55 open roles
on FindRole.
Listed pay typically runs
$209,000–$313,000
across 39 roles with salary data.
As a Staff Machine Learning Engineer on Snap Inc.’s Search team, you will lead the development of advanced ranking systems that enhance user experience through personalized and relevant search results. Your responsibilities include designing and implementing machine learning models for relevance, personalization, intent understanding, and engagement optimization, while ensuring these systems scale efficiently across multiple objectives such as freshness, diversity, and safety. You will collaborate closely with product managers and data scientists to define success metrics and experiment strategies, analyze user behavior data, and build robust offline evaluation frameworks. Proficiency in Python, C++, Java, Scala, TensorFlow, PyTorch, and large-scale data processing tools like Spark is essential, along with a strong background in machine learning fundamentals and experience translating research into production systems.
Skills
Python
TensorFlow
PyTorch
Spark
Flink
Beam
Java
Scala
C++
JAX
A/B testing
learning-to-rank
LambdaMART
neural ranking models
transformer-based rankers
large-scale data processing
ML infrastructure
online experimentation
model monitoring
feature pipelines
training infrastructure
serving systems
multi-objective optimization
LLMs
foundation models
semantic search
natural language understanding
retrieval-augmented generation
What you'll do
Lead the design and development of machine learning models for Search ranking.
Own major ranking initiatives from problem definition through launch and iteration.
Develop and improve ranking models using advanced techniques like deep retrieval and neural ranking.
Build robust offline evaluation, online experimentation, and model monitoring frameworks.
Improve feature pipelines, training infrastructure, serving systems, and model iteration velocity.
Provide technical leadership across teams and mentor engineers working on ML ranking systems.
What we're looking for
Extensive experience (8+ years) in machine learning, including model development for relevance ranking and personalization.
Proficiency in Python, C++, Java, Scala or similar languages, with expertise in large-scale data processing tools like Spark, TensorFlow, PyTorch.
Strong ability to design and implement robust offline evaluation frameworks and online experimentation strategies for ML models.
Proven track record of leading complex technical projects across multiple teams and influencing architecture decisions.
Deep understanding of machine learning fundamentals such as supervised learning, ranking models, embeddings, deep learning, and optimization techniques.
Experience in building low-latency ML serving systems and improving production model reliability.