Jobs / United States / Salesforce INC
Lead Machine Learning Engineer
Salesforce INC · 🇺🇸 2 Locations
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 1,544 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 498 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Salesforce INC paid sponsored hires in similar roles45 certified filings for “Senior Technical Support Engineer” (Software Quality Assurance Analysts and Testers) in TX: $118k–$148k, median $132k. Most were filed at wage level III (67%) — 3 lottery entries, ≈46% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — Salesforce INC
The US Department of Labor certified 1,544 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 498 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Salesforce INC →
About the 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. Lead member of technical staff - Machine Learning Engineering Job Category: Software Engineering Job Details About Salesforce We’re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good – you’ve come to the right place. We are a foundation machine learning platform team within the Trust Intelligence Platform organization with a main focus to build and accelerate scalable and resilient machine learning pipelines across the security engineering organization. We are seeking a highly motivated, hands-on lead machine learning engineer with strong business understanding to define and execute the technical ML strategy. This role involves full lifecycle development and optimization of ML pipelines, with a strong focus on MLOps, infrastructure-as-code, CI/CD, and thorough monitoring. The lead will manage multiple ML pipelines, work closely with cross-functional teams, mentor others, and requires deep expertise in various ML technologies to deliver measurable business impact within a security and compliance framework. A critical component of this role is the proven ability to productize Generative AI solutions and agentic workflows to actively enhance the security posture and resilience for the organization and all Salesforce customers. Your impact: Define and drive the technical ML strategy, emphasizing robust and performant model architectures and MLOps practices. Lead the end-to-end development of ML pipelines, focusing on automated retraining workflows and model optimization for cost and performance. Own a portfolio of multiple machine learning pipelines within security and compliance. Implement infrastructure-as-code, CI/CD pipelines, and MLOps automation with a focus on model monitoring and drift detection. Design and implement comprehensive monitoring solutions for model performance, data quality, and system health. Collaborate with Data Science, Data Engineering, Research, and Product Management teams to deliver scalable ML solutions with measurable impact. Provide technical leadership in ML engineering best practices and mentor junior machine learning engineers. Required skills: Masters or PhD in a quantitative field Extensive experience (6+ years) in the end-to-end lifecycle of multi-model machine learning systems, from design and development to large-scale deployment. Deep understanding and practical application of containerization (Docker) and workflow orchestration (Kubernetes, Apache Airflow) for automated ML pipelines. Mastery of Python programming, including proficiency in leading ML frameworks (Ten