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Applied Scientist LMTS

Salesforce

Applied Scientist LMTS

Salesforce

San Francisco, CA

·

On-site

·

Full-time

·

1mo ago

Benefits & Perks

Flexible work arrangements

Professional development budget

401(k) matching

Generous paid time off and holidays

Comprehensive health, dental, and vision insurance

Parental leave

Flexible Hours

Learning

Healthcare

Parental Leave

Required Skills

Python

React

JavaScript

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.
Job Category
Software Engineering
Job Details

About Salesforce

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn't a buzzword - it's a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.
Ready to level-up your career at the company leading workforce transformation in the agentic era? You're in the right place! Agentforce is the future of AI, and you are the future of Salesforce.

Overview of the Role

Join an agile team with deep startup roots. We operate as a high-velocity 'startup-within-Salesforce,' following our recent acquisition. You'll be managed by the same founders and engineers who built the original company, offering the autonomy of a small team backed by the global scale and trust of Salesforce. You will have the unique opportunity to build a '0 to 1' product with the founding team, while leveraging Salesforce's world-class Data Cloud and security infrastructure to drive immediate global impact at scale.
Global supply chains still rely on slow, manual processes-email, spreadsheets, and fragmented data. The economy moves fast but supply chains don't, creating an inefficiency that affects the $13T of goods shipped annually and is one of the largest untapped opportunities in modern enterprise.
Agentforce Supply Chain is reimagining the supply chain with an AI-powered platform for designing, automating, and running end-to-end business processes, with seamless collaboration through familiar channels like email. For Salesforce, this represents a massive growth opportunity in the back office, with innovations that flow into the front office. Customers are clamoring for more, rapidly expanding their use cases as we enter an exhilarating growth phase. As one user put it: "I've been waiting for this for 20 years."

The Impact You Will Have As a Lead Applied Scientist for the Agentforce Supply Chain Foundational model team, you will be instrumental in the development of a novel foundational model that we are building for complex tabular datasets.

  • Develop, implement, and deploy AI models to solve real-world complex problems in global supply chain and manufacturing.
  • Apply and build upon state-of-the-art machine learning technologies, stay-up-to-date with current developments in the AI field, and use your expertise to inspire AI applications across the organization.
  • Collaborate with cross-functional teams including Engineering, Design, Product Management, and industry experts to build high-quality product features that can be used by major companies around the world.
  • Establish and scale robust evaluation frameworks by creating domain-specific benchmarks and automated testing suites; implement production telemetry pipelines to monitor real-world model performance, data drift, and system health.

Requirements

  • Masters/PhD in Computer Science or quantitative field with research in AI

  • 5-7+ years of industry or post-Masters/PhD experience developing, building, and deploying Machine Learning models for real-world scenarios

  • Strong experience in deep learning and graphs or network theory. Experience with at least one other form of machine learning, such as natural language processing, reinforcement learning, computer vision, LLMs, etc.

  • Deep expertise in training large-scale models across distributed systems using frameworks like Py Torch Distributed (DDP/FSDP), Deep Speed, or Torch Titan on GPU clusters.

  • Proficiency with Python and Py Torch, Tensorflow, or JAX

  • Demonstrated ability not only to use state-of-the-art machine learning techniques but also to innovate upon them.

  • End-to-End MLOps Proficiency: Hands-on experience building and maintaining production-grade ML pipelines using tools such as Kubeflow, Airflow, or MLflow for experiment tracking, versioning, and automated retraining.

Preferred Qualifications

  • Experience with supply chain, manufacturing, or related problems

Unleash Your Potential:

When you join Salesforce, you'll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best, and our AI agents accelerate your impact so you can do your best. Together, we'll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future - but to redefine what's possible - for yourself, for AI, and the world.
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About Salesforce

Salesforce

A cloud-based software company that provides customer relationship management software and applications.

10,001+

Employees

San Francisco

Headquarters

$243B

Valuation

Reviews

4.0

16 reviews

Work Life Balance

3.0

Compensation

3.5

Culture

2.5

Career

3.0

Management

2.0

35%

Recommend to a Friend

Pros

Competitive compensation packages

Remote work flexibility

Good benefits (headphone/internet reimbursement)

Cons

Ongoing layoffs and job insecurity

Poor refresher/yearly stock grants

Condescending interview processes

Salary Ranges

45 data points

Junior/L3

L3

L5

L6

Junior/L3 · Associate Data Engineer

1 reports

$120,510

total / year

Base

$92,700

Stock

-

Bonus

-

$120,510

$120,510

Interview Experience

5 interviews

Difficulty

3.4

/ 5

Offer Rate

20%

Experience

Positive 20%

Neutral 20%

Negative 60%

Interview Process

1

Application Review

2

Recruiter Screen

3

Technical Phone Screen

4

Onsite/Virtual Interviews

5

Final Interview Panel

6

Offer

Common Questions

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Past Experience