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Machine Learning Software Engineer Intern Jobs in Santa Clara, CA

Tenex.AI Inc

Software Engineer Intern

San Jose, CA · On-site

We are seeking a highly motivated and versatile Full Stack Engineer Intern to join our core ... Participate in all aspects of software development activities, including design, coding, code ...

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All JobsMachine Learning Software Engineer Intern JobsMachine Learning Software Engineer Intern Jobs in Santa Clara, CA

Machine Learning Software Engineer Intern information

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$15

$29

$45

How much do machine learning software engineer intern jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for machine learning software engineer intern in Santa Clara, CA is $29.85, according to ZipRecruiter salary data. Most workers in this role earn between $24.28 and $33.89 per hour, depending on experience, location, and employer.

What does a machine learning software engineer intern do?

A Machine Learning Software Engineer Intern assists in the development, testing, and deployment of machine learning models and algorithms. Their responsibilities typically include data preprocessing, model training, evaluation, and collaborating with senior engineers to integrate machine learning solutions into software products. Interns may also contribute to research, documentation, and code optimization, gaining hands-on experience with real-world machine learning projects. This role provides a valuable opportunity to apply academic knowledge in a professional setting and learn from experienced engineers.

What are the key skills and qualifications needed to thrive as a machine learning software engineer intern?

To thrive as a Machine Learning Software Engineer Intern, you need a solid understanding of programming (especially Python), machine learning algorithms, and data structures, ideally supported by coursework or relevant projects. Familiarity with frameworks such as TensorFlow or PyTorch, experience using version control systems like Git, and knowledge of cloud platforms are highly valuable. Critical thinking, eagerness to learn, and effective communication help interns collaborate with teams and adapt to new challenges. These skills and qualities are crucial for developing robust ML solutions, integrating with production systems, and contributing meaningfully to real-world projects.
What are the most commonly searched types of Machine Learning Software Engineer jobs in Santa Clara, CA? The most popular types of Machine Learning Software Engineer jobs in Santa Clara, CA are:
What cities near Santa Clara, CA are hiring for Machine Learning Software Engineer Intern jobs? Cities near Santa Clara, CA with the most Machine Learning Software Engineer Intern job openings:
Machine Learning Software Engineer Intern jobs near you

Software Engineer - Machine Learning

FocusKPI Inc.

Mountain View, CA • On-site

$95 - $110/hr

Contractor

Re-posted 28 days ago

Job description

FocusKPI is seeking a Software Engineer - Machine Learning to join one of our clients, a high-tech SaaS company.
We are looking for an experienced Machine Learning Engineer to lead the development of prompt-injection and prompt-safety models to protect the client's downstream agentic AI systems across phones, the cloud, and XR/AR. You will design, train, and deploy classifier and guardrail models (both cloud-based and hybrid on-device) that screen agent inputs and outputs for injection attacks, unsafe content, and policy violations. A core part of the role is post-training these models using RLHF, DPO, and related optimization techniques to push detection accuracy and false-positive rates beyond what off-the-shelf solutions can achieve.
Work Location: Mountain View, CA (Onsite role, 5 days/week onsite)
Duration: 12-month contract with potential to extend the contract depending on your performance & budget
Pay Range: $95 - 110/hr
**No C2C resumes are considered**
Position Responsibilities:
  • Design and train prompt-injection detection models and prompt-safety classifiers that operate on both inputs to and outputs from the client's agentic AI systems.
  • Build hybrid deployment pipelines that split safety inference between on-device (phone, XR/AR) and cloud, optimizing for latency, privacy, and detection coverage.
  • Apply post-training techniques (e.g., RLHF, reward modeling, policy optimization) to optimize guardrail model performance, calibration, and robustness against adaptive adversaries.
  • Curate and generate adversarial training data: direct and indirect prompt injections, jailbreaks, tool-use exploits, and unsafe-output cases drawn from red-teaming and production signals.
  • Build evaluation harnesses that measure attack success rate, false-positive rate, latency, and on-device footprint across model iterations and threat categories.
  • Partner with agent, device, and platform teams to integrate safety models into mobile-use agents, XR/AR assistants, and cloud agentic workflows, and to close the loop from production incidents back into training data.
  • Work cross-functionally with security researchers, modeling teams, and product engineers; document methods and, where appropriate, contribute to patents and publications.
Qualifications:
  • M.S. or Ph.D. in Computer Science, Machine Learning, Electrical Engineering, or a related field; or B.S. with equivalent industry experience.
  • 3+ years of industry experience in ML engineering or applied AI research, with demonstrated ownership of production ML systems post-master's degree graduation.
  • 2+ years of industry experience in software engineering post-master's degree graduation.
  • Strong proficiency in Python and PyTorch (or JAX/TensorFlow), with solid software engineering fundamentals (version control, testing, and reproducible experimentation).
  • Hands-on experience post-training LLMs with RLHF, DPO, RLAIF, or reward modeling, including reward design, preference data curation, and training stability.
  • Hands-on experience training and deploying classifier or guardrail models for safety, content moderation, abuse detection, or adversarial robustness.
  • Familiarity with prompt injection, jailbreak, and agentic AI threat models, and with distributed training frameworks (DeepSpeed, FSDP, Accelerate).
Preferred Qualifications:
  • Experience building safety or moderation systems for agentic AI: tool-use guardrails, indirect prompt injection defenses, or output filtering for autonomous agents.
  • Experience with red-teaming, adversarial data generation, or automated attack pipelines (e.g., GCG, PAIR, generator-critic frameworks).
  • Experience with on-device or edge ML deployment (ExecuTorch, Core ML, TFLite, MLC-LLM, vendor NPU toolchains) and model compression (quantization, distillation, pruning) for safety models.
  • Experience with telemetry, logging, or user-facing data systems on mobile, XR/AR, or consumer platforms, including privacy-preserving handling of user data (e.g., anonymization, on-device processing, federated approaches).
  • Publications at top-tier ML/NLP/security venues (NeurIPS, ICML, ICLR, ACL, EMNLP, USENIX Security, IEEE S&P), patents, or open-source contributions in the safety, alignment, or AI security space.
Education:
  • M.S. in Computer Science, Machine Learning, Electrical Engineering, or a related field with 3 years of experience post graduation
  • Ph.D. in Computer Science, Machine Learning, Electrical Engineering, or a related field with publications in the AI/ML domain and 1 year of experience post-graduation

**No C2C resumes are considered**
Thank you!
FocusKPI Hiring Team
Founded in 2010, FocusKPI, Inc. (FocusKPI) is a data science and technology firm specializing in predictive analytics practice and methodologies. FocusKPI is a US company headquartered in Silicon Valley, California, with an East Coast office in Boston, Massachusetts.
NOTICE: Please be aware of fraudulent emails regarding job postings, job offers and fake checks. FocusKPI's recruiting team will strictly reach out via @focuskpi.com email domain. If you have received fraudulent emails now or in the past, please report it to https://reportfraud.ftc.gov/ .
The domain @focuskpijobs.com is fraudulent and not related to FocusKPI. Please do not not reply or communicate to anyone with @focuskpijobs.com.
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About FocusKPI

Sourced by ZipRecruiter

Industry

Computing infrastructure providers, data processing, web hosting

Company size

51 - 200 Employees

Headquarters location

Santa Clara, CA, US

Year founded

2010

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