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About This Role
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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.
Join an agile team with deep startup roots. We operate as a high-velocity 'startup-within-Salesforce,' following our recent acquisition. You’ll be working alongside the founding team and engineers in an organization 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 in one of the fastest growing verticals within Salesforce.
Global supply chains still rely on slow, manual processes—email, spreadsheets, and fragmented data. Nowhere does modernizing them matter more than in the public sector, where the scale and stakes are highest.
Missionforce Operations is reimagining the public sector 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 into public sector back office processes, 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.”
What you’ll do
As a Senior AI Angineer Agentforce & Missionforce Operations, you will help shape both the product and the engineering team. You will partner closely with product leaders, sector experts, and platform and forward-deployed engineers to deliver trusted AI systems for mission-critical public-sector workflows.
Missionforce Operations is Agentforce Operations’ public-sector counterpart: rebuilt from the ground up to bring core capabilities to some of the world's most sensitive operating environments. In this role, you will lead the development of AI products and platform capabilities that perform complex tasks reliably, securely, and cost-effectively in environments where trust is mission-critical and data classification requirements are uncompromising.
You will tackle a broad range of challenges, from building AI features tailored to public-sector customers, to designing systems with security, compliance, reliability, and operational constraints at their core. You will operate with wide autonomy, build alignment across engineering and product stakeholders, and raise the bar for technical quality, execution, and mentorship across the team.
Responsibilities
Day-to-Day & Project Execution
- Partner with product and forward-deployed engineers to build reliable new features for our AI platform.
- Lead development of intelligent agents that complete complex supply chain tasks with reliability and consistency
- Design and implement planning, orchestration, and evaluation systems that enable agents to execute multi-step workflows autonomously.
- Make and drive architectural decisions for mission-critical, highly available systems that operate across varied and constrained deployment environments.
Establish engineering practices for model evaluation, AI safety, observability, reliability, and cost management. *
Long-Term Goals & Strategic Impact
- Partner with Product Management and executive leadership to translate product vision into multi-year technical roadmaps.
- Guide technical strategy for AI model deployment, safety constraints, reliability frameworks, and evaluation methodologies.
- Drive the adoption of enterprise-grade observability, operational excellence, and cloud infrastructure practices.
- Identify and mitigate technical risks related to security, compliance, scale, availability, and model behavior.
Help shape the organization by raising the engineering bar, contributing to hiring, and fostering a culture of continuous learning and high ownership. *
Required Qualifications
Candidates must have the following to be considered:
- B.S. in Computer Science or equivalent with coursework in Artificial Intelligence (M.S. is a plus).
- 4+ years of industry experience in Software Engineering, with a focus in AI/ML.
- Strong proficiency in multiple programming languages, such as Python, Go, Java, or C++.
- Experience designing and operating production-grade distributed systems, APIs and data models.
- Deep expertise in model evaluation, including designing custom benchmarks, automated evaluation suites, and production telemetry for monitoring model quality, drift, reliability, and safety.
- Experience building production systems with LLM orchestration frameworks, along with the judgment to extend or move beyond those frameworks when reliability requirements demand it.
- Demonstrated ability to lead complex technical initiatives, make sound architectural decisions, and deliver through ambiguity.
- Proven ability to collaborate across engineering, product, customer-facing, and executive stakeholders to build alignment and drive decisions.
- Experience mentoring engineers and raising the quality of technical execution across a team.
- Excellent written and verbal communication skills.
Enthusiasm for learning and growing as a software engineer, and for helping your peers do the same. *
Preferred Qualifications
- Experience building products for regulated industries, particularly the public sector or environments with strong security, compliance, and data-sovereignty requirements.
- Familiarity with developing for classified or limited-connectivity environments, including Department of Defense Impact Levels such as IL6.
- Experience with enterprise-grade observability platforms, such as
infrastructure as code, and cloud-native deployment practices.
- Experience with containerization and orchestration technologies, such as Docker and Kubernetes.
- Experience building AI products for supply chain, logistics, manufacturing, or operational workflows.
Contributions to open-source software, patents, publications, or other notable technical work. *
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.

The typical base salary range for this position is $148,500 - $223,900 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $178,900 - $246,000 annually.

The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.
Salary Context
This $148K-$223K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($186K) sits 12% below the category median. Disclosed range: $148K to $223K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Salesforce AI Hiring
Salesforce has 16 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Software Engineer, AI Architect. Positions span San Francisco, CA, US, Bellevue, WA, Indianapolis, IN, US. Compensation range: $223K - $344K.
Location Context
AI roles in San Francisco pay a median of $263,811 across 2,416 tracked positions. That's 24% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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