Location: San Francisco, California or Seattle, Washington Employment Type: Full time
Location Type: Hybrid Department: Engineering
ABOUT THE TEAM
The Machine Learning Platform 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 give Sift's Data Science and ML Engineering teams the tooling 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 as a backfill for our outgoing lead. This isn't a maintenance role — it's a chance to modernize a foundational platform at a moment when the stakes are high: our biggest deals increasingly come down to who can win a competitive proof-of-value the fastest, and this team's tooling determines whether we win it.
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.
- Build the tooling and metrics that let Sift run faster, sharper customer proof-of-value engagements, online and offline, so we win competitive bake-offs instead of losing them to slow iteration.
- 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 or machine learning engineering teams.
- 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. 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.
BENEFITS AND PERKS
- Competitive total compensation package
- Medical, dental and vision coverage
Let’s build it together
At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion
Sourced directly from the company's job board — apply on their site.