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Machine Learning Engineer, Multimodal - Integrity

Job in San Jose, Santa Clara County, California, 95199, USA
Listing for: Ellis Technologies, Inc.
Full Time position
Listed on 2026-06-20
Job specializations:
  • IT/Tech
    Machine Learning/ ML Engineer, AI Engineer (Applied/Software)
  • Engineering
    AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 156000 - 316800 USD Yearly USD 156000.00 316800.00 YEAR
Job Description & How to Apply Below
Position: Machine Learning Engineer, Multimodal - Intelligent Integrity

Location:

San Jose

Employment Type:

Regular

Job Code: A193136A

Responsibilities
  • Focus on ad content understanding and security, conduct algorithm R&D for large model implementation, and apply LLM/MLLM and other AIGC technologies to e-commerce and short‑video ad content understanding to build a next‑gen large model‑based commercial intelligence review system.
  • Deepen multimodal understanding tech for text, audio, video, live streaming, etc., optimize model decision‑making for high‑accuracy autonomous risk judgment, and implement interpretable CoT generation for traceable model decisions.
  • Explore RL/Agent applications in multimodal review scenarios, track AIGC cutting‑edge trends, and deliver algorithm innovation and engineering implementation tailored to commercial business needs.
  • Develop dedicated multimodal content understanding models to empower ad intent recognition, intelligent rule retrieval and accurate risk judgment, enhance advertisers' creation experience, reduce non‑compliant content non‑detection risks, and protect ad ecosystem security.
  • Qualifications
  • Bachelor’s degree or above in CS, AI, Mathematics, Statistics or related majors, with 1+ year of algorithm R&D/project implementation experience in content understanding or AIGC.
  • Solid ML/DL theoretical foundation, in‑depth understanding of MLLM/LLM, CV, NLP, multimodal fusion and Agent technologies; strong mathematical skills, excellent self‑learning and problem‑solving abilities; project implementation experience preferred.
  • Proficient in PyTorch/Tensor Flow, with hands‑on experience in large model training, fine‑tuning and inference deployment; excellent engineering capabilities, mastery of Python/C++.
  • Familiar with technical principles and applications of multimodal large models; experience in content understanding, ad analysis, multimodal representation learning or intelligent review preferred.
  • Job Information

    The base salary range for this position in the selected city is $156,000 - $316,800 annually.

    Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

    Benefits

    Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short‑term and long‑term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

    Equal

    Employment Opportunity

    For Los Angeles County (unincorporated) Candidates:
    Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

    • Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
    • Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems;
    • Exercising sound judgment.
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