Multi-task/multi-objective models (shared encoders + task heads) that jointly learn relevance ... with online testing and attribution beyond CTR. * Strong coding (Python) and data fluency (SQL ...
OR · On-site
$141K - $242K/yr
You will design algorithms that seamlessly fuse data across a complex sensor suite to provide real ... wheel encoders, etc. - Architect and develop mathematical models to be used in factor graph ...
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Online Data Encoder information
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$5.75 - $11.75
12% of jobs
$13.61 is the 25th percentile. Wages below this are outliers.
$11.75 - $17.74
43% of jobs
$17.74 - $23.74
19% of jobs
$24.42 is the 75th percentile. Wages above this are outliers.
$23.74 - $29.73
12% of jobs
$29.73 - $35.72
3% of jobs
$35.72 - $41.72
2% of jobs
$41.72 - $47.71
1% of jobs
$47.71 - $53.71
1% of jobs
$53.71 - $59.70
0% of jobs
$59.70 - $65.69
3% of jobs
$65.69 - $71.69
4% of jobs
$5
$27
$71
How much do online data encoder jobs pay per hour?
What is a data encoder work from home?
What are the key skills and qualifications needed to thrive in the Online Data Encoder position, and why are they important?
To thrive as an Online Data Encoder, strong typing skills, attention to detail, and basic computer literacy are essential, often supported by a high school diploma or equivalent. Familiarity with spreadsheet programs (such as Microsoft Excel or Google Sheets), data management systems, and sometimes basic database software is preferred. Reliability, time management, and the ability to maintain focus during repetitive tasks are standout soft skills for this position. These competencies are crucial for ensuring the accuracy, security, and efficiency of data entry operations in a remote or online work environment.
What job makes $10,000 a month without a degree?
How much do data encoders typically make?
How to become an online encoder?
What is an Online Data Encoder job?
An Online Data Encoder is responsible for inputting, updating, and maintaining digital information in a database or system. This role requires accuracy, speed, and attention to detail to ensure that data is correctly recorded and organized. Encoders often work with spreadsheets, company databases, or specialized software to process large amounts of information efficiently. The job may also involve verifying data for errors and ensuring confidentiality and security of sensitive information.
What types of data will I typically be handling as an Online Data Encoder, and how does this impact my daily responsibilities?
As an Online Data Encoder, you will usually work with a variety of information, including customer records, sales figures, survey responses, or inventory logs, depending on the employer and industry. Your primary responsibility is to accurately input, update, and manage this data in digital databases or spreadsheets, often under set deadlines. You may also be asked to review and verify information for accuracy or help generate periodic reports. The type of data you handle can influence the specific procedures you follow and may require you to learn unique data management software or adhere to confidentiality standards. Being diligent and organized will help ensure data integrity and contribute to the smooth operations of your team.

Other
Posted 16 days ago
Instacart rating
6.7
Based on 29 frontline employees who took The Breakroom Quiz
Job description
Overview
The Search & Personalization ML team is Instacart's engine for state-of-the-art multi-task, multi-objective ranking-unifying search, discovery, recommendation, ads, and merchandising into a single value-aware platform. Partnering with world-class engineers, scientists, and PMs, we build the ranking backbone that powers every pixel of the shopping journey, optimizing not just for clicks, but for incremental GTV, basket lift, and retention over the long run.
What We're Building
- Foundational Ranking Backbone Models: Multi-task/multi-objective models (shared encoders + task heads) that jointly learn relevance, conversion, margin contribution, churn risk, and ad quality, enabling consistent decisions across search and recommendations.
- Value-Aware Optimization: Uplift and long-horizon value models that steer decisions toward incrementality and LTV, with calibrated constraints on quality, diversity, fairness, and spend pacing-plus guardrails for safe exploration.
- LLM-Enhanced Retrieval & Features: Using LLMs to enrich query and item semantics for long-tail recall, generate features for cold-starts, and feed the ranker with reasoning-rich context, while remaining the source of truth for final ordering.
Our commitment to AI innovation is reflected in our recent publications and research contributions to the field.
About the Job
- Architect the ranking backbone that unifies query understanding, personalization, multi-objective ranking, ads, and merchandising into a single adaptive platform.
- Design and build a search autosuggest system optimized for personalization and value-based relevance.
- Design long-horizon objective functions (e.g., incrementality, LTV, habit formation) and build uplift/causal value models that move beyond short-term engagement.
- Develop production-grade Multi-Task Learning (e.g., shared encoders, MMOE/PLE task heads) to jointly learn relevance, propensity, margin, and churn risk-ensuring calibration, constraints, and explainability.
- Own the inference layer: goal-aware re-rankers, diversity and quality constraints, safe exploration, and millisecond-class latency optimization.
- Advance evaluation practices: online experiments, long-horizon cohort metrics, counterfactual evaluations, and attribution pipelines for tracking incremental GTV and retention.
- Partner across ads, infrastructure, product, and design teams to translate business goals into ranking policies and measurable ROI.
- Mentor ML engineers to build expertise in ranking, causal inference, and scalable serving systems.
About You
Minimum Qualifications
- 5+ years applying ML at scale (3+ years in technical leadership), with a proven track record improving ranking or recommendation systems in production.
- Demonstrated success in applying multi-objective or constrained optimization to balance relevance, revenue, margin, and user experience; experience with online testing and attribution beyond CTR.
- Strong coding (Python) and data fluency (SQL/Pandas), with expertise in classic ML techniques (e.g., XGBoost) and deep learning frameworks (TensorFlow/PyTorch).
- Excellent analytical skills and strong cross-functional communication abilities.
Preferred Qualifications
- Expertise in multi-task learning architectures (e.g., MMOE/PLE, shared encoders), calibration, counterfactual evaluation, uplift/causal modeling, and/or contextual bandits for exploration.
- Experience building low-latency ranking services, including feature stores, caching, vector + lexical retrieval, re-ranking, and A/B testing infrastructure, with expertise in constraint-aware inference.
- Hands-on experience with LLMs as feature/recall enhancers (e.g., embeddings, adapter tuning) while maintaining clarity on when the ranker should arbitrate.
What Instacart employees say
Pay
Benefits
Hours and flexibility
Workplace
Get the full story on Breakroom
About Instacart
Sourced by ZipRecruiter
Instacart, based in San Francisco, CA, US, operates within the retail industry, specifically grocery delivery and pick-up service. It is recognized as a pioneer in this field, delivering fresh groceries from local stores directly to customers' doors. The company, which launched its services in 2012, continues to pioneer change in the online grocery shopping sector through its commitment to cutting-edge technology, new business ideas, and dedicated service.
Industry
Technology, communication and media
Company size
10,000+ Employees
Headquarters location
San Francisco, CA, US
Year founded
2012