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About This Role
*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.
We are seeking a highly skilled, hands\-on, and deeply technical Software Development Engineers to help build our AI Governance platform from the ground up. This is a critical senior role responsible for designing and developing both the front\-end and back\-end foundations of a platform that enables safe, trusted, and scalable AI deployment across the enterprise.
This role will directly support our number one value: Trust
You will help architect and deliver a platform that spans governance, intake workflows, lifecycle management, monitoring, observability, risk controls, and operational tooling for AI systems and agents. We are looking for a builder who is comfortable wearing multiple hats across software engineering, cloud infrastructure, platform engineering, developer tooling, and user experience. You should be energized by ambiguity, excited to build systems from scratch, and motivated by solving difficult problems at the intersection of AI, governance, trust, observability, and enterprise scale.
What You’ll Do
- Key Responsibilities:
- Full Stack Platform Development: Lead the end\-to\-end design, development, and scaling of the AI governance platform, building both the front\-end and back\-end components that support enterprise wide AI governance
- AI\-Assisted Engineering: Use AI development tools such as Claude and other coding assistants as part of the software development lifecycle to accelerate delivery, improve code quality, prototype faster, and enhance engineering productivity
- AWS Cloud Infrastructure Development: Design and build secure, scalable, and resilient cloud native infrastructure on AWS to support platform services, governance workflows, system integrations, and application performance at enterprise scale
- ML and AI Platform Services: Build and support platform capabilities that enable AI and machine learning systems to be governed, monitored, tracked, and managed throughout their lifecycle, including services that support model and agent operations
- CI/CD Delivery Process Knowledge: Bring practical knowledge of CI/CD concepts, automated testing, and deployment workflows, and release management practices to help ensure the platform can be delivered reliably across environments
- Architecture and Technical Design: Define and drive the overall platform architecture, including service design, API strategy, data flows, integration patterns, event\-driven workflows, and system scalability considerations
- Monitoring and Operational Visibility: Develop monitoring capabilities that provide insight into system health, application performance, workflow execution, service reliability, and platform usage across the governance ecosystem
- Observability and Telemetry: Build observability components that capture logs, metrics, traces, and runtime telemetry across platform services, enabling deeper diagnostics, issue detection, root cause analysis, and ongoing operational intelligence
- Generative AI Platform Development: Assist with designing and developing Generative AI capabilities as part of the platform, including LLM powered features, intelligent workflows, agent\-based functionality, and other AI native applications
- Technical Leadership and Ownership: Provide strong technical leadership across the stack, establish engineering standards, influence design decisions, mentor other engineers, and take ownership of delivering a strategic platform from the ground up
- Cross Functional Collaboration: Partner closely with product, architecture, security, compliance, governance, and engineering stakeholders to translate business goals and trust requirements into scalable technical solutions
What We’re Looking For
- 10\+ years of professional software development experience with significant depth across both front\-end and back\-end development
- Strong hands\-on expertise in full stack development, including modern front\-end frameworks, API design, distributed systems, and back\-end application development.
- Proven experience building complex platforms or enterprise applications from scratch
- Deep experience with AWS and cloud\-native architecture, including designing scalable, secure, and production grade systems.
- Strong experience with platform engineering, developer infrastructure, and production software delivery practices
- Demonstrated ability to build and scale CI/CD pipelines, automated frameworks, and deployment workflows
- Experience building systems with strong monitoring, observability, logging, telemetry, and operational insight capabilities
- Strong architectural judgment
- Experience working in environments where security, compliance, governance, and auditability are important design considerations
- Comfort working across ambiguity and leading technical execution in highly visible, high\-impact initiatives
- Excellent collaboration and communication skills
- Demonstrated experience using Generative AI as part of the software development lifecycle
Preferred Qualifications (Bonus Points):
- Experience with Salesforce Ecosystem
- Experience building or supporting AI governance, model governance, risk, trust, compliance, or observability platforms
- Experience with Gen AI applications, LLM\-powered systems, agentic workflows, and model evaluation frameworks.
- Experience with MLOps, LLMOps, or AI platform engineering, including model lifecycle tolling and development controls
- Familiarity with data privacy, model risk, or regulatory considerations in enterprise AI environments
- Experience in regulated or trust sensitive industries where system reliability, governance, and control are critical
- Experience designing systems for auditability, lineage, traceability, and evidence management
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.
Accommodations
If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form .
Please note that Salesforce uses artificial intelligence (AI) tools to help our recruiters assess and evaluate candidates’ resumes and qualifications throughout the recruiting process. Humans will always make any candidate selection and hiring decisions. Please see our Candidate Privacy Statement for more information about how we use your personal data and your rights, including with regard to use of AI tools and opt out options.
Posting Statement
Salesforce is an equal opportunity employer and maintains a policy of non\-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.
In the United States, compensation offered will be determined by factors such as location, job level, job\-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: https://www.salesforcebenefits.com.Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.
At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions.\&\#xa;\&\#xa;The typical base salary range for this position is $172,500 \- $260,100 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $207,800 \- $285,800 annually.\&\#xa;\&\#xa;The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.
Salary Context
This $172K-$285K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Salesforce, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($229K) sits 28% above the category median. Disclosed range: $172K to $285K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Salesforce AI Hiring
Salesforce has 10 open AI roles right now. They're hiring across AI/ML Engineer. Positions span McLean, VA, US, San Francisco, CA, US, Indianapolis, IN, US. Compensation range: $223K - $401K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 1,990 tracked positions. That's 26% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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