Salesforce

1 month ago

Principal AI Engineer

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

Lead the design and scale of Salesforce’s ML/AI platform, building agent harness infrastructure, sandboxed execution environments, and evaluation frameworks to support autonomous AI agents across marketing, sales, service, and product growth. Own end-to-end lifecycle from experiments to production, enabling high-quality, low-latency agent systems and continuous improvement through flywheel principles.

Location: San Francisco, Palo Alto, Chicago, New York, Seattle, Bellevue

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Sr. Manager level

Key Responsibilities

•Design and build agent harness infrastructure: the scaffolding that wraps LLM calls, manages tool use, handles retries, enforces policy, and feeds results back into iterative improvement loops
•Implement agentic loop patterns with multi-turn reasoning, tool orchestration, memory management, and structured output handling as reusable platform primitives
•Build the agent flywheel: automated pipelines that collect agent traces, surface regressions, route failures to evaluation, and close the loop from production signal back to prompt/model improvement
•Own the end-to-end lifecycle from agent experiment to production deployment, including versioning, rollout controls, and rollback mechanisms
•Build sandboxed execution environments for agent tools with isolating code execution, API calls, and file system access so agents can act without unconstrained blast radius

Pay and Benefits

Salary: USD 172,500 - 313,700 annually

Equity and Bonus:Equity

Perks:Health InsuranceDentalVision401kEquity

Key Requirements

•9+ years as a Platform Engineer, ML Infrastructure Engineer, or Software Engineer
•Demonstrated experience building agent harness infrastructure using agentic loops, tool orchestration, structured output handling, multi-turn conversation management
•Hands-on experience with agent evaluation frameworks like Braintrust, LangSmith, or equivalent, including building eval datasets, running automated regression suites, and tracking quality metrics over time
•Strong understanding of sandboxing and safe agent execution like isolation patterns, tiered autonomy, blast radius controls
•Experience with context Engineering as it relates to Agent orchestration.

Experience:9+ yearsAIMLEnterprise softwareCRMSaaS

Skills:Problem-solvingCommunicationCollaboration

Languages:English

Tech Stack:PythonAWSGitHub ActionsArgoCDTerraformDockerKubernetesGrafanaLLMLangSmithBraintrust

Company Brief

Salesforce

Provides a leading cloud-based customer relationship management (CRM) platform with sales, service, marketing, analytics, and integration tools that empower businesses to manage customer relationships and digital transformation at scale.

Industry: SaaS

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: San Francisco, United States

Founded: 1999

Glassdoor: 4.1

Website LinkedIn Glassdoor

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Salesforce

1 month ago

Principal AI Engineer

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

Location: San Francisco, Palo Alto, Chicago, New York, Seattle, Bellevue

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Sr. Manager level

Key Responsibilities

•Design and build agent harness infrastructure: the scaffolding that wraps LLM calls, manages tool use, handles retries, enforces policy, and feeds results back into iterative improvement loops
•Implement agentic loop patterns with multi-turn reasoning, tool orchestration, memory management, and structured output handling as reusable platform primitives
•Build the agent flywheel: automated pipelines that collect agent traces, surface regressions, route failures to evaluation, and close the loop from production signal back to prompt/model improvement
•Own the end-to-end lifecycle from agent experiment to production deployment, including versioning, rollout controls, and rollback mechanisms
•Build sandboxed execution environments for agent tools with isolating code execution, API calls, and file system access so agents can act without unconstrained blast radius

Pay and Benefits

Salary: USD 172,500 - 313,700 annually

Equity and Bonus:Equity

Perks:Health InsuranceDentalVision401kEquity

Key Requirements

•9+ years as a Platform Engineer, ML Infrastructure Engineer, or Software Engineer
•Demonstrated experience building agent harness infrastructure using agentic loops, tool orchestration, structured output handling, multi-turn conversation management
•Hands-on experience with agent evaluation frameworks like Braintrust, LangSmith, or equivalent, including building eval datasets, running automated regression suites, and tracking quality metrics over time
•Strong understanding of sandboxing and safe agent execution like isolation patterns, tiered autonomy, blast radius controls
•Experience with context Engineering as it relates to Agent orchestration.

Experience:9+ yearsAIMLEnterprise softwareCRMSaaS

Skills:Problem-solvingCommunicationCollaboration

Languages:English

Tech Stack:PythonAWSGitHub ActionsArgoCDTerraformDockerKubernetesGrafanaLLMLangSmithBraintrust

Company Brief

Salesforce

Industry: SaaS

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: San Francisco, United States

Founded: 1999

Glassdoor: 4.1

Website LinkedIn Glassdoor