Staff Machine Learning Engineer

Prior to applying please note:

• We are currently unable to provide visa sponsorship for this position (including H-1B, OPT, or other employment-based visas). Candidates must be legally authorized to work in the United States without employer sponsorship now or in the future.
• This role is hybrid requiring 2 days in office at our San Francisco hub every Tuesday & Wednesday (located at 130 Sutter St).

About the Role

Machine Learning is a cornerstone at Taskrabbit, and we’re looking for a Staff Machine Learning Engineer to take technical ownership of our core ranking system. Every job request on the platform flows through it, making this one of the most consequential ML systems we run.

This is a hands-on technical leadership role. You’ll operate as the primary architect and engineer for the ranking system — defining the system direction, driving the roadmap, solving the hardest problems, and creating leverage for the engineers around you. You’ll also serve as the primary technical interface for Product, Data Science, Risk, and Commerce on all things matching.

What we need is a single technical owner who can hold the architecture, translate strategy into engineering action, and mentor the team as the system scales.

What You’ll Work On:

• Ranking System Ownership: Own the architecture, model design, experiment strategy, and production reliability of the core ranking system end-to-end. You are the system’s primary technical decision-maker.
• Cross-functional Technical Leadership: Serve as the primary technical point of contact for Product, Data Science, Risk, and Commerce on matching and ranking questions. Actively participate in architecture reviews; give clear, well-reasoned recommendations and convince stakeholders on technical tradeoffs.
• Model Reliability: Build automated retraining pipelines, rollback capability, and skew detection — the foundations that enable safe experimentation at speed and eliminate silent model drift.
• Ranking Quality & Debiasing: Address position bias and other systemic biases in the ranking pipeline to produce cleaner training data and unlock downstream experimentation.
• Client Intent Signals: Leverage filter-based signals from high-booking user cohorts (69–72% higher booking rate) to drive direct conversion lift from untapped behavioral data.
• Personalization: Surface return-client signals, prior Tasker preferences, and preference modeling to improve IAR for the highest-value booking segment.
• Platform Thinking: Identify where ML can add value beyond the ranking system and scope those opportunities for the broader team. Make build-vs-buy recommendations and contribute to long-term ML architecture at Taskrabbit.
• Team Development & Thought Leadership: Mentor the junior engineer, elevate seniors through code and design reviews, and raise the technical bar across the team. Beyond day-to-day guidance, you bring a transformational perspective — introducing new methodologies, challenging existing patterns, and shaping how the team thinks about ML problems. You’re a sought-out guide and a catalyst for how the team evolves.

Who You Are:

We welcome applicants from a variety of backgrounds and experiences. Below gives you a sense of how we’re thinking about what you’ll need to be successful in the role.

• BS, MS, or PhD in Computer Science, Statistics, Operations Research, or a related quantitative field.
• 8+ years of industry experience building and deploying production-grade ML systems, with a track record of owning a system end-to-end — not just contributing to one.
• Deep expertise in ranking, recommender systems, or two-sided marketplace ML. Experience with debiasing, A/B experimentation at scale, and the subtleties of feedback loops in production systems.
• You think architecturally. You can hold the full picture of a complex system — data pipelines, feature stores, training infrastructure, serving, monitoring — and make principled decisions about how they fit together.
• You write and review code at a high standard. Your code reviews are insightful and educational; your implementations set the bar for quality and maintainability on the team.
• You can do critical R&D. When a problem is novel and the solution is unclear, you have the depth to explore it systematically and the judgment to know when to go deeper vs. ship.
• You have a vision for how state-of-the-art advances in ML — from large language models to modern deep ranking architectures — can transform what a traditional ranking system is capable of, and the judgment to chart a practical path toward that future.
• You’re an effective cross-functional partner. You communicate technical tradeoffs clearly to non-technical stakeholders, proactively identify upstream and downstream implications, and build trust with partners through reliability and transparency.
• You motivate and elevate the people around you. You give kind, direct feedback, mentor with generosity, and help others grow without creating dependency.
• Strong proficiency in Python and SQL. Hands-on experience with ML libraries (PyTorch, TensorFlow, scikit-learn, LightGBM/XGBoost) and modern data infrastructure (Kafka, Airflow, Snowflake/BigQuery, Docker/Kubernetes). Familiarity with dbt is a plus.
• Experience with Infrastructure as Code (Terraform) and CI/CD pipelines.
• Proficiency in using AI coding tools (e.g., Claude Code, Augment, or similar) across the full software development lifecycle — from designing and generating code to testing, monitoring, and releasing software.

Compensation & Benefits:

At Taskrabbit, our approach to compensation is designed to be competitive, transparent, and equitable. Total compensation consists of base pay + bonus + benefits + perks. The base pay range for this position is $170,000 - $225,000. This range is representative of base pay only, and does not include any other total cash compensation amounts, such as company bonus or benefits. Final offer amounts may vary from the amounts listed above and will be determined by factors including, but not limited to, relevant experience, qualifications, geography, and level.