Staff Machine Learning Engineer, Taskrabbit
$170-225k
+ Bonus
More information about location
2 days a week in office ( Sutter St, CA)
Outsourcing marketplace for everyday tasks
Be an early applicant
Outsourcing marketplace for everyday tasks
1001+ employees
Be an early applicant
$170-225k
+ Bonus
More information about location
2 days a week in office ( Sutter St, CA)
1001+ employees
Company mission
To transform lives, one task at a time, by connecting millions globally with trusted Taskers every year.
Role
Who you are
- 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
What the job involves
- 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
- 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
Our take
Back in 2011, TaskRabbit took full advantage of a growing trend - that technology enables consumers to do more commerce in their homes than ever before. Its marketplace connects users to "Taskers" that can perform virtually any role customers require, such as heavy lifting and cleaning.
TaskRabbit's product is diverse, with the potential to enter any commerce sector in the world. In 2017, the company was acquired by IKEA, which gave it the resources to undergo a data-driven European expansion which included launching in Germany and France, as well as increasing services in densely-populated UK areas.
By developing internationally, while also emphasising its offerings as a delivery service, the company has set itself up on the world stage. Having continued to grow and expand its offerings, TaskRabbit remains a key player in the gig economy, with every sign that it will continue its successes into the future.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
3% employee growth in 12 months
Company
Funding (2 rounds)
Jul 2012
$13.8m
Dec 2011
$18.8m
Total funding: $32.6m
Company benefits
- Remote first and highly connected
- Employee-covered health care
- 401k with match
- Flexible PTO
- Paid holidays & recharge weeks
- Wellbeing allowance and free access to HeadSpace and Spring Health
- Paid parental leave, adoption assistance, and reproductive health assistance
- Speaker series, career paths, and an annual education allowance
- IKEA discount, Taskrabbit credit, and productivity stipend
Company HQ
Potrero Hill, San Francisco, CA
Leadership
Ania Smith
(CEO)Several years of experience in leadership at companies like Expedia, Uber, Airbnb and Walmart.
Salary benchmarks
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