Taskrabbit

4 weeks ago

Staff Machine Learning Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 4 hours agoStatus: Live

Job Summary

Lead the architecture and engineering of Taskrabbit’s core ranking system in a hybrid San Francisco-based team. Own model design, production reliability, and experimentation at scale; mentor engineers, partner with Product/Data Science/Risk/Commerce, and drive ML roadmap to improve ranking quality, debiasing, and personalization for high-value bookings.

Location: San Francisco

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Own the architecture, model design, experiment strategy, and production reliability of the core ranking system end-to-end.
•Serve as the primary technical point of contact for Product, Data Science, Risk, and Commerce on matching and ranking questions; participate in architecture reviews and advise on technical tradeoffs.
•Build automated retraining pipelines, rollback capability, and skew detection to enable safe experimentation at speed and prevent silent model drift.
•Address position bias and other systemic biases in the ranking pipeline to produce cleaner training data and improve downstream experimentation.
•Mentor the team, raise the technical bar through code/design reviews, and shape ML practices and architecture across the group.

Pay and Benefits

Salary: USD 170,000 - 225,000 annually

Perks:Health Insurance401kEducation StipendWellness StipendIkea DiscountsTime Off

Key Requirements

•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.
•Strong architectural thinking across data pipelines, feature stores, training infrastructure, serving, and monitoring.
•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 across the full software development lifecycle.

Experience:8+ yearsTwo-sided marketplace MLRanking systems

Education:Bachelor's

Skills:LeadershipMentorshipCommunicationCode reviewsProblem-solving

Languages:English

Tech Stack:PythonSQLPyTorchTensorFlowScikit-learnLightGBMXGBoostKafkaAirflowSnowflakeBigQueryDockerKubernetesDbtTerraform

Company Brief

Taskrabbit

Operates an online marketplace that connects users with local freelance labor for household tasks such as furniture assembly, moving, cleaning, and home repairs, matching vetted 'Taskers' with customers for on‑demand local services.

Industry: Online Marketplaces

Company Size: Large (251 to 1,000 employees)

Growth: Established Company

Headquarters: San Francisco, United States

Founded: 2008

Glassdoor: 3.9

Website LinkedIn

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Taskrabbit

4 weeks ago

Staff Machine Learning Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 4 hours agoStatus: Live

Job Summary

Location: San Francisco

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Own the architecture, model design, experiment strategy, and production reliability of the core ranking system end-to-end.
•Serve as the primary technical point of contact for Product, Data Science, Risk, and Commerce on matching and ranking questions; participate in architecture reviews and advise on technical tradeoffs.
•Build automated retraining pipelines, rollback capability, and skew detection to enable safe experimentation at speed and prevent silent model drift.
•Address position bias and other systemic biases in the ranking pipeline to produce cleaner training data and improve downstream experimentation.
•Mentor the team, raise the technical bar through code/design reviews, and shape ML practices and architecture across the group.

Pay and Benefits

Salary: USD 170,000 - 225,000 annually

Perks:Health Insurance401kEducation StipendWellness StipendIkea DiscountsTime Off

Key Requirements

•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.
•Strong architectural thinking across data pipelines, feature stores, training infrastructure, serving, and monitoring.
•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 across the full software development lifecycle.

Experience:8+ yearsTwo-sided marketplace MLRanking systems

Education:Bachelor's

Skills:LeadershipMentorshipCommunicationCode reviewsProblem-solving

Languages:English

Tech Stack:PythonSQLPyTorchTensorFlowScikit-learnLightGBMXGBoostKafkaAirflowSnowflakeBigQueryDockerKubernetesDbtTerraform

Company Brief

Taskrabbit

Industry: Online Marketplaces

Company Size: Large (251 to 1,000 employees)

Growth: Established Company

Headquarters: San Francisco, United States

Founded: 2008

Glassdoor: 3.9

Website LinkedIn