Jobs / United States / Taskrabbit INC
Staff Machine Learning Engineer - Ranking System
Taskrabbit INC · 🇺🇸 New York, New York, United States; San Francisco, California, United States
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 8 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest May 2026) — the step every H-1B hire needs first. USCIS also records 3 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Taskrabbit INC paid sponsored hires in similar roles1 certified filing for “Staff Data Engineer” (Database Architects) in CA: $175k–$175k, median $175k. Most were filed at wage level II (100%) — 2 lottery entries, ≈31% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — Taskrabbit INC
The US Department of Labor certified 8 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest May 2026) — the step every H-1B hire needs first. USCIS also records 3 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Taskrabbit INC →
About the role
About Taskrabbit: Taskrabbit is a marketplace platform that conveniently connects people with Taskers to handle everyday home to-do’s, such as furniture assembly, handyman work, moving help, and much more. At Taskrabbit, we want to transform lives one task at a time. As a company we celebrate innovation, inclusion and hard work. Our culture is collaborative, pragmatic, and fast-paced. We’re looking for talented, entrepreneurially minded and data-driven people who also have a passion for helping people do what they love. Together with IKEA, we’re creating more opportunities for people to earn a consistent, meaningful income on their own terms by building lasting relationships with clients in communities around the world. Taskrabbit is a hybrid company with employees distributed across the US and EU and a Built In — Best Places to Work (2022, 2023, 2024, 2025) continually ranked across multiple national and regional categories. Join us at Taskrabbit, where your work will be meaningful, your ideas valued, and your potential unleashed! This role is hybrid requiring 2 days in office at our San Francisco or NYC hub every Tuesday & Wednesday. 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 probl