# FewerJobs export - 4 curated jobs Generated: 2026-06-20T04:43:49.153Z Source: https://fewerjobs.com ## Filters applied - **q**: Braintrust - **quality_floor**: default - **match_401k_strict**: true - **parental_strict**: true - **non_birth_strict**: true - **pto_strict**: true - **include_older**: false - **verified_benefits_only**: true - **apply_url_verified**: false - **page**: 1 - **per_page**: 100 - **sort**: relevance ## Jobs ### Principal AI Engineer - Salesforce - Location: California - San Francisco; Washington - Seattle; Illinois - Chicago; California - Palo Alto; New York - New York; Washington - Bellevue (unspecified) - Salary: $238K-$345K - Posted: 2026-05-29 - Parental leave: 26 weeks (not source-backed) - Non-birth-parent leave: 12 weeks (not source-backed) - 401(k) match: listed (source-backed) - Source: https://www.askebsa.dol.gov/FOIA%20Files/2024/Latest/F_SCH_H_2024_Latest.zip - Apply: https://careers.salesforce.com/en/jobs/jr344074/principal-ai-engineer/ - Excerpt: Principal AI Engineer California - San Francisco; Washington - Seattle; Illinois - Chicago; California - Palo Alto; New York - New York; Washington - Bellevue We are seeking a highly skilled AI Platform Engineer to play a pivotal role in building the next generation of our ML/AI platform that doesn't just support ML models, but powers autonomous AI agents at enterprise scale. This role sits at the intersection of platform infrastructure and agent systems engineering. You'll build and maintain the core infrastructure, CI/CD pipelines, and platform services that underpin our machine learning initiatives and go further in designing the harnesses, sandboxes, and evaluation frameworks that let AI agents be developed, tested, and trusted in production. You'll work on systems that directly impact marketing, sales, service, and product growth verticals across the organization. This isn't a traditional infrastructure role. You should be comfortable wearing multiple hats of software engineering, agent systems design, and evaluation tooling. We're looking for engineers who think in flywheels: build →evaluate → improve → ship → repeat. What You'll Do Agent Harness & Flywheel Engineering 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 ### Lead AI Engineer, Data Solutions - Salesforce - Location: California - San Francisco; Washington - Seattle; Illinois - Chicago; New York - New York (unspecified) - Salary: $208K-$286K - Posted: 2026-05-29 - Parental leave: 26 weeks (not source-backed) - Non-birth-parent leave: 12 weeks (not source-backed) - 401(k) match: listed (source-backed) - Source: https://www.askebsa.dol.gov/FOIA%20Files/2024/Latest/F_SCH_H_2024_Latest.zip - Apply: https://careers.salesforce.com/en/jobs/jr341433/lead-ai-engineer-data-solutions/ - Excerpt: Lead AI Engineer, Data Solutions California - San Francisco; Washington - Seattle; Illinois - Chicago; New York - New York We are looking for a Lead AI Engineer to build next-generation AI and ML systems at Salesforce. This role focuses on developing intelligent decisioning systems and building an agent flywheel-a system of feedback loops that continuously evaluate, optimize, and improve agent performance over time. This is an applied AI role with strong data and systems ownership. You will build models and agents and the data pipelines and evaluation loops that enable continuous learning in production. What You'll Do Build the Agent Flywheel Design feedback loops that enable agents and ML systems to improve from real-world outcomes Track outcomes (engagement, conversion, quality) and evaluate agent performance Build pipelines that collect and structure agent traces into training and evaluation datasets Drive continuous improvement via prompting, policies, model selection, and fine-tuning Develop ML & Agent Systems Build and deploy ML models (classification, ranking, forecasting, recommendation) Design AI agents that combine LLM reasoning, tool usage, and ML decisioning Implement reusable patterns for multi-step reasoning, tool orchestration, and structured outputs Integrate models and agents into business-critical workflows Own Data & Model Pipelines Design and build scalable data pipelines (batch and near real-time) for training, evaluation, and inference Transform raw interaction data into features, labels, and evaluation datasets Enable continuous retraining and evaluation through tightly coupled data + model pipelines Ensure data quality, consistency, and reliability Evaluation & Experimentation Build offline and online evaluation frameworks Develop ### AI Platform Engineer - Salesforce - Location: Mexico - Mexico City (unspecified) - Salary: Not disclosed - Posted: 2026-05-08 - Parental leave: 26 weeks (not source-backed) - Non-birth-parent leave: 12 weeks (not source-backed) - 401(k) match: listed (source-backed) - Source: https://www.askebsa.dol.gov/FOIA%20Files/2024/Latest/F_SCH_H_2024_Latest.zip - Apply: https://careers.salesforce.com/en/jobs/jr341755/ai-platform-engineer/ - Excerpt: AI Platform Engineer Mexico - Mexico City About the Role We are seeking a highly skilled and motivated Lead AI Platform Engineer to play a pivotal role in the development of our ML/AI platform. This role will be instrumental in building, maintaining, and scaling the core infrastructure, platform services, and CI/CD pipelines that underpin our machine learning initiatives and product launches. You will work on critical projects that directly impact our marketing, sales, service, and product growth verticals of the organization. This isn't a traditional infrastructure role. You should be open to wearing multiple hats ; infrastructure, software engineering, UI/UX development, and AI-native tooling. We're looking for engineers who don't just build platforms for AI , they use AI to build the platform. You ship faster because you've made Claude Code, autonomous agents, and AI-powered developer tools part of your daily workflow, not an experiment you're still evaluating. We want innovative, out-of-the-box thinkers who aren't afraid to experiment, build complex systems, and tackle challenges across the full stack with AI as the force multiplier at every layer. What You'll Do AI-Native Engineering & Developer Velocity Use Claude Code (CLI) as a primary engineering tool writing, refactoring, debugging, and reviewing infrastructure and platform code with AI pair programming as the default, not the exception. Build and publish reusable AI tools, skills, and integrations in internal tool marketplaces so that platform capabilities are discoverable and reusable across engineering teams. Design and deploy autonomous agents that accelerate developer workflows, self-healing CI pipelines, automated ### Lead AI Engineer - Salesforce - Location: Mexico - Mexico City (hybrid) - Salary: Not disclosed - Posted: 2026-05-12 - Parental leave: 26 weeks (not source-backed) - Non-birth-parent leave: 12 weeks (not source-backed) - 401(k) match: listed (source-backed) - Source: https://www.askebsa.dol.gov/FOIA%20Files/2024/Latest/F_SCH_H_2024_Latest.zip - Apply: https://careers.salesforce.com/en/jobs/jr341428/lead-ai-engineer/ - Excerpt: Lead AI Engineer Mexico - Mexico City Lead AI Engineer (Mexico City) Data Solutions Org Hybrid We are looking for a Lead AI Engineer to drive the development of next-generation AI and ML systems at Salesforce. This role owns the design and evolution of intelligent decisioning systems and expands into building a broader agent flywheel (a system of self-improving feedback loops that continuously evaluate, optimize, and evolve agent performance). This role sits on the applied side but requires strong data and systems engineering depth - you will build not just models and agents, but the data pipelines, evaluation loops, and lightweight system scaffolding that allow them to continuously improve in production. You will build production-grade ML models, embed them into agent workflows, and define how agents learn from real-world outcomes. This is a hands-on, high-impact role focused on shipping systems that directly influence agent performance, efficiency, revenue, and customer experience. What You'll Do 1) Build the Agent Flywheel Design and implement feedback loops that enable agents and ML models to self-improve over time Develop systems for: Outcome tracking (e.g., engagement, conversions, resolution quality) Agent evaluation (LLM + deterministic + human-in-the-loop signals) Iterative optimization (prompting, policies, model selection, fine-tuning) Build pipelines that collect and structure agent traces (inputs, tool usage, intermediate steps, outputs) into high-quality training and evaluation datasets Close the loop from production signals → evaluation → model/prompt improvements 2) Develop Production ML & Agent Systems Build and deploy application-specific ML models (classification, ranking, forecasting, recommendation, etc.) Design and implement --- Source-backed benefit claims include source links; other benefit values are labeled separately.