Senior Engineering Manager, ML Platform

Location:

San Francisco, California or Seattle, Washington

Employment Type:

Full time, Hybrid

About the Team

The Machine Learning team — internally known as "Potato Radius" — builds the training pipelines, feature infrastructure, and evaluation systems behind every score Sift returns, across more than 700 customers and a trillion-plus events a year. We are Sift's Data Science and ML Engineering team responsible to ship models fast, prove they work, and trust them in production.

What We're Looking For

• We're hiring a Senior Engineering Manager to lead this team. You're a manager who's inspiring and technical, and who knows how to bring focus to what matters now without losing sight of the long term. You value collaboration and transparency, operate with a get-stuff-done mindset, and bring the technical depth and bias for shipping to spot the manual, brittle, or duplicated work that's quietly slowing the team down. You build a culture of mentorship, give regular and constructive feedback, set clear goals, and grow your team by hiring effectively.
• Projects You Might Lead
• Launch a unified model evaluation framework that gives Data Science fast, trustworthy, apples-to-apples comparisons before a model ever reaches production or shadow traffic.
• Evolve core feature infrastructure — including a new global feature store — to improve accuracy and unlock faster experimentation.
• Bring a fresh approach to model configuration, replacing tribal knowledge and manual gating with auditable, safely-controlled releases.
• Introduce agentic, AI-assisted tooling into customer investigations, automating repetitive data pulls and validation so analysts spend their time on judgment calls, not manual digging.
• Build automation that detects an active fraud attack, adjusts score calibration in real time, and cleanly reverts once it subsides.

What You'll Do

• Lead and grow the team: Own the roadmap, execution, and quality of the systems that train, evaluate, and serve Sift's ML models in production, leading a team of ML platform engineers and data scientists.
• Stay technical: Review designs, unblock engineers on hard problems, and make credible calls on architecture and trade-offs.
• Drive customer POVs: Partner directly with strategic customers and Sales/Solutions Engineering on technical proof-of-value engagements, translating customer requirements into platform capabilities.
• Reduce technical debt: Drive a sustained, measurable reduction in technical debt across the ML platform, balancing new feature delivery with the health of existing systems.
• Build evaluation frameworks: Mature the systems that give Data Science and ML Engineering fast, trustworthy signals on model quality before and after deployment.
• Automate the ML lifecycle: Identify repeatable, manual processes across training, evaluation, deployment, and monitoring, and drive their automation.
• Partner cross-functionally: Align platform investments with business priorities alongside Data Science, Core Infrastructure, Product, and Customer Success.
• Technical Stack
• GCP, AWS, Spark, Kafka, Kubernetes, Docker, Databricks, Python

What Would Make You a Strong Fit

8+ years of overall hands-on engineering experience, including 4+ years managing software, data science or machine learning engineering teams.

Experience managing Data Scientist and/or in-depth knowledge for data science.

Deep technical fluency in machine learning systems: model training pipelines, feature engineering, model serving, and evaluation at production scale.

Proven track record leading technical customer engagements or POVs, including direct interaction with enterprise customers.

Demonstrated success reducing technical debt in a live, high-traffic production system without stalling feature delivery.

Experience designing or scaling evaluation frameworks (offline and/or online) for machine learning models.

Track record of identifying manual, repeatable engineering processes and driving their automation.

Experience hiring, mentoring, and developing engineering talent.

B.S. or MS/Phd in Computer Science (or related technical discipline), or equivalent practical experience.

Bonus Points

Experience with large-scale distributed ML infrastructure such as Spark, Flink, Databricks, or similar.

Familiarity with fraud detection, risk, or trust & safety domains.

Hands-on experience with GCP or AWS ML infrastructure.

Experience with streaming architectures (e.g., Kafka) and containerized/orchestrated deployments (Docker, Kubernetes).

Familiarity with using AI coding assistants (e.g., Claude Code) to accelerate development.

Our Interview Process

Introduction interview:

30- 45 minutes with a recruiter to discuss your background and the role.

Hiring Manager interview:

30- 45 minutes with the hiring manager to explore your fit for the position.

Hybrid onsite loop with the team:

approximately 4–5 hours covering system design, a technical deep dive, a cross-functional stakeholder scenario, and values & behavior.

A little about us

Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Brands including DoorDash, Yelp, and Poshmark rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at sift.com and follow us on LinkedIn.