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Remote Bagging Machine Operator Jobs in California

Posting Type Remote/Hybrid Job Overview WHO WE ARE Relativity is a leading legal data intelligence ... experience building and operating machine learning models at scale. We work closely with ...

Data Engineer III 70756-1

Menlo Park, CA ยท On-site +1

$134K - $162K/yr

Remote Inference Orchestration: Own the systems for remote ML model inference orchestration within ... Demonstrated track record of building and operating production data pipelines that invoke ML models ...

Senior AI/ML Engineer

Sunnyvale, CA ยท On-site +1

$124K - $170K/yr

Remote/Hybrid: This role is based remotely but if you live within a 50-mile radius of Sunnyvale, CA ... The Data Labeling Engineering team designs, builds, and operates hybrid human/machine data labeling ...

Senior Data Scientist

Los Angeles, CA ยท On-site +1

$106.31 - $132.31/hr

Remote (US/PST) Duration: 12+ months long term project Compensation: $106.31 - 132.31/hr. Work ... This role blends rigorous experimentation, applied machine learning, and strategic insight ...

Senior Software Engineer, MLOps

Irvine, CA ยท On-site +1

$131K - $173K/yr

You will work closely with machine learning engineers, robotics engineers, and infrastructure teams ... Experience deploying and operating ML systems for robotics or real-world physical systems.

Senior Software Engineer, MLOps

Irvine, CA ยท On-site +1

$131K - $173K/yr

You will work closely with machine learning engineers, robotics engineers, and infrastructure teams ... Experience deploying and operating ML systems for robotics or real-world physical systems.

Senior Software Engineer, MLOps

Irvine, CA ยท On-site +1

$131K - $173K/yr

You will work closely with machine learning engineers, robotics engineers, and infrastructure teams ... Experience deploying and operating ML systems for robotics or real-world physical systems.

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Remote Bagging Machine Operator information

What are the key skills and qualifications needed to thrive as a Remote Bagging Machine Operator, and why are they important?

To thrive as a Remote Bagging Machine Operator, you need mechanical aptitude, attention to detail, and a high school diploma or equivalent. Familiarity with automated packaging equipment, remote monitoring software, and basic troubleshooting tools is typically required. Strong problem-solving skills, effective communication, and reliability help operators excel in managing machines remotely and responding quickly to issues. These skills ensure efficient production, minimize downtime, and maintain product quality in automated manufacturing environments.

What are some common challenges faced by remote bagging machine operators and how can they be addressed?

Remote bagging machine operators often encounter challenges such as troubleshooting machinery issues without on-site support and maintaining consistent product quality remotely. Effective communication with on-site teams and having clear protocols for remote monitoring are essential for success. Staying organized and proactive in reporting and addressing maintenance needs can help minimize downtime. Leveraging digital tools, regular virtual check-ins, and ongoing training can further enhance performance and ensure smooth operations.

What is a Remote Bagging Machine Operator?

A Remote Bagging Machine Operator is responsible for overseeing and controlling bagging machinery from a remote location, often using computer systems or specialized software. Their duties include monitoring equipment performance, troubleshooting issues, and ensuring products are accurately bagged and prepared for distribution. They may also perform routine maintenance and communicate with on-site staff to address any operational concerns. This role is essential in industries such as manufacturing, agriculture, and food processing to maintain efficiency and product quality.

What is the difference between Remote Bagging Machine Operator vs Remote Packaging Technician?

AspectRemote Bagging Machine OperatorRemote Packaging Technician
CredentialsHigh school diploma, training in machine operationHigh school diploma, training in packaging processes
Work EnvironmentManufacturing or warehouse setting, operating bagging machines remotelyManufacturing or warehouse, handling packaging tasks remotely
Industry UsageFood, pharmaceuticals, logisticsFood, consumer goods, pharmaceuticals

The Remote Bagging Machine Operator and Remote Packaging Technician roles share similar credentials and work environments, often within manufacturing industries. The main difference lies in the specific tasks: the operator focuses on running bagging machinery, while the technician handles broader packaging processes. Both roles require attention to detail and safety awareness, making them closely related in the packaging industry.

What job categories do people searching Remote Bagging Machine Operator jobs in California look for? The top searched job categories for Remote Bagging Machine Operator jobs in California are:
What cities in California are hiring for Remote Bagging Machine Operator jobs? Cities in California with the most Remote Bagging Machine Operator job openings:
Senior Applied Scientist

Senior Applied Scientist

Relativity

Los Angeles, CA โ€ข On-site, Remote

Other

Medical, Retirement

This job post hasย expired today.ย Applications are no longer accepted.


Job description

Posting Type

Remote/Hybrid

Job Overview

WHO WE ARE
Relativity is a leading legal data intelligence company building technology that helps users organize data, discover the truth, and act on it with confidence. Our AI-powered, cloud platform, RelativityOne, transforms massive volumes of complex information into actionable insights for litigation, investigations, regulatory inquiries, data breach responses, and other high-stakes legal work where accuracy, trust, and accountability are critical.
Every year, the global justice system benefits from insights generated by Relativity AI across billions of documents. We are just getting started on our journey to use AI to improve the outcome of every discovery, investigation, and analysis performed on our platform.
At Relativity, we develop AI guided by our AI Principles. These principles ensure we build AI with clear purpose, empower customers with transparency and control, treat fairness and privacy as first principles, protect customer data by design, and act with a high standard of responsibility and accountability.
WHAT WE DO
Relativity's AI organization is focused on exploration, experimentation, and turning cutting-edge research into real-world impact. We believe innovation requires experimentation, learning, and iteration. Our teams experiment, evaluate, ship, and learn continuously while maintaining a strong commitment to responsible AI.
Applied Science Team
The Applied Science team operates at the core of Relativity's AI development. Our team includes specialists with advanced postgraduate training and deep experience building and operating machine learning models at scale. We work closely with engineering, product, design, data engineering, machine learning operations, and LLM engineering teams to translate complex AI research into production-ready features used by legal professionals around the world.

Job Description and Requirements

ABOUT THE ROLE

As a Senior Applied Scientist, Generative AI, you will design, build, and deploy generative and machine learning models that power Relativity's next generation of AI-driven product capabilities. You will collaborate closely with applied scientists, engineers, product managers, and designers to build models that help legal professionals organize data, discover the truth, and act on it with confidence.

This role balances research, development, and operational responsibility. You will contribute to Relativity's portfolio of transformational generative AI technologies while adhering to our responsible AI principles and ensuring models perform reliably in real-world, high-stakes environments.

WHAT YOU'LL DO

  • Develop machine learning and generative AI models that ship as customer-facing product features
  • Collaborate closely with engineers to write production-quality code and contribute across the full model deployment lifecycle
  • Design and evaluate models that operate at very large scale, including search and retrieval systems spanning hundreds of millions to billions of documents
  • Contribute to internal standards, processes, and tooling for building, evaluating, and deploying generative AI systems
  • Partner with Product and Data teams to assemble, curate, and synthesize datasets for model development and evaluation
  • Conduct rigorous experimentation, model evaluation, and iteration to improve model quality, explainability, safety, and performance
  • Collaborate across AI, engineering, and product teams to ensure models integrate effectively into larger systems
  • Apply Relativity's AI Principles to ensure responsible, fair, secure, and transparent AI development
  • Communicate complex data science and machine learning concepts clearly and effectively to collaborators with diverse technical backgrounds

WHAT WE'RE LOOKING FOR

Required

  • Experience building search or retrieval systems operating at the scale of hundreds of millions of documents
  • Experience developing and applying generative AI models as part of larger, domain-specific systems
  • Experience across the full machine learning lifecycle, including experimentation, evaluation, deployment, and iteration
  • Experience working in containerized environments using Kubernetes-based tooling and workflows
  • Interest in or experience with the legal industry, eDiscovery, or the broader justice system
  • Strong programming ability in a language such as Python
  • Comfort working in UNIX-based environments using command-line tools
  • Ability to communicate complex data science concepts thoughtfully and inclusively to a wide range of stakeholders

Preferred

  • Master's degree in Computer Science or a quantitative field plus 2 years of relevant industry experience
  • OR Ph.D. in Computer Science or a quantitative field
  • OR the equivalent of 5 years of relevant academic and/or industry experience
  • Experience building and deploying systems that leverage large language models
  • Experience contributing to shared data science or ML engineering standards, tooling, or best practices

WHY WE COULD BE A GREAT FIT

Impactful Mission

  • Your work directly contributes to improving outcomes across the global justice system by helping customers uncover critical insights in massive, complex datasets.

AI at Real Scale

  • You'll work on some of the largest and most complex AI systems in the legal technology market, operating at significant data and computational scale.

Growth and Collaboration

  • You'll collaborate closely with experienced applied scientists, engineers, and product leaders while continuing to grow your expertise in generative AI and production machine learning systems.

Responsible AI Culture

  • You'll be part of an organization deeply committed to building AI that is ethical, transparent, secure, and accountable.

Inclusive Environment

  • We value diverse perspectives, backgrounds, and ways of thinking, and believe they make our teams and products stronger.

Compensation and Benefits

  • Competitive compensation, health and retirement benefits, discretionary time off (DTO), parental leave for primary and secondary caregivers, company-wide breaks, wellness resources, and an equity program.

Relativity is committed to competitive, fair, and equitable compensation practices.

This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.

The expected salary range for this role is between following values:

$146,000 and $218,000

The final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position.

Required Skills:

Algorithms, C++ Programming Language, Computer Vision, Data Science, Deep Learning, Machine Learning (ML), Natural Language, Python (Programming Language), Researching, Statistical Models