Sr. Staff Machine Learning Engineer, Content Quality

About the role

You define the technical roadmap for integrity and trust signals on the platform, setting long term strategy that balances safety with user expression. You align with multiple product and engineering teams to translate abstract ideas into production grade solutions that directly impact user safety and experience. This role requires you to own the end to end lifecycle of content quality models, from problem discovery through deployment and measurement. You will design safety mechanisms specifically for Generative AI and conversational use cases, including alignment methods and vision language model safeguards. Your work will directly inform improvements to ranking, search, and creator tools through defined signal effectiveness reviews. You will establish standardized quality practices by integrating with partner products, ensuring consistent enforcement across client products at scale. A core part of this role is building measurement frameworks that highlight edge cases and drive iterative improvements in content workflows. You will maintain internal playbooks that codify engineering best practices and clarify system behavior for collaborators, promoting experiment designs that advance innovation speed while respecting safety thresholds and user trust.

Key facts

What you'll do

Architect and deliver the blueprint for content integrity signals, coordinating closely with downstream systems to enable safe adoption at every layer of the stack.

Design safety mechanisms for Generative AI and conversational use cases, including alignment methods and vision language model safeguards that are robust to emerging threats.

Define and validate signal effectiveness through review outcomes, informing improvements to ranking, search, and creator tools with data driven decision making.

Partner with ML engineers to turn concepts into production signals, covering problem formulation, feature design, validation strategies, and deployment pipelines that meet latency targets.

Establish standardized quality practices through partner integrations, ensuring consistent enforcement across client products while maintaining flexibility for product specific needs.

Build measurement frameworks that highlight edge cases and drive iterative improvements in content workflows, enabling teams to understand failure modes quickly.

Maintain internal playbooks that codify engineering best practices and clarify system behavior for collaborators, reducing ambiguity and increasing execution speed.

Promote experiment designs that advance innovation speed while respecting safety thresholds and user trust, ensuring changes are measurable and reversible when necessary.

Implement scalable data pipelines that support high volume, latency sensitive environments, leveraging big data technologies to process diverse streams efficiently.

Own the documentation of model behavior and decision traceability, providing clear narratives for stakeholders to understand tradeoffs and rationale behind key choices.

Champion the adoption of new tooling such as AI coding assistants to speed model development and validation, improving quality and throughput of delivered solutions.

Utilize LLM powered tools for documentation search, experiment analysis, SQL exploration, and workflow acceleration to reduce manual overhead and accelerate learning.

Define and maintain strict eligibility criteria for signal inclusion in production systems, ensuring only high quality, validated signals are promoted.

Collaborate with cross functional teams including ML, product, and operations to deliver on shared priorities, aligning on metrics and success criteria.

Champion the use of advanced measurement and scalability skills required for high volume, latency sensitive environments, ensuring robustness under peak load.

Requirements

Demonstrated ability to lead technical strategy in complex, large scale organizations, managing ambiguity and driving outcomes without direct oversight.

Deep expertise in content modeling for consumer internet products that process diverse, high volume data streams, including text, images, and multimodal inputs.

Hands on experience using Generative AI methods to speed model development and validation, reducing cycle time while maintaining rigorous quality standards.

Proven capacity to work cross functionally with ML, product, and operations teams to deliver on shared priorities, balancing competing demands effectively.

Strong ownership of documentation and decision traceability while managing multiple stakeholders, ensuring transparency and reproducibility of results.

Advanced measurement and scalability skills required for high volume, latency sensitive environments, including monitoring, alerting, and performance optimization.

Bachelor's or Master's degree in a relevant field such as computer science or equivalent experience, demonstrating foundational knowledge of algorithms, systems, and data structures.

Solid machine learning knowledge, including training, evaluation, and deployment practices, with an understanding of pitfalls like leakage, overfitting, and distribution shift.

Practical background with big data technologies such as Hadoop, Spark, Kafka, or Flink, showing ability to build reliable data pipelines that scale to petabyte scale.

Production experience deploying machine learning at scale in live services, beyond theoretical understanding, including debugging issues in distributed systems.

Experience with safety and integrity problems in social products, including content classification, anomaly detection, and policy enforcement mechanisms.

Familiarity with evaluation frameworks and experimental methods, enabling rigorous measurement of model impact on user behavior and platform health.

Strong coding skills in Python, with ability to write clean, maintainable, and tested code that can be reviewed and extended by other engineers.

Comfort working in fast paced environments where requirements evolve quickly, and you can adapt solutions while maintaining long term architectural thinking.

Nice to have

Familiarity with AI coding assistants like Cursor, Copilot, Codex, or similar tools for development, debugging, testing, and refactoring to accelerate delivery.

Experience using LLM powered tools for documentation search, experiment analysis, SQL exploration, and workflow acceleration to reduce manual overhead and improve insight generation.

Practical notes

Employment Type: Full-time;

Location: San Francisco, CA, US; Please verify all details on the official application page.