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Ml Inference Jobs in Utah (NOW HIRING)

NextDeavor Inc.

Product Security Engineer

Lehi, UT · On-site

$67.61 - $84.51/hr

Hands-on penetration testing experience for AI/ML and LLM-powered products, including chat interfaces and inference APIs. * Prior participation in bug bounty programs as a researcher. * Familiarity ...

Xenter, Inc.

Sr. Data Engineer

Draper, UT · On-site

$107K - $128K/yr

... inference, and an LLM-agentic layer that queries both. We are looking for an engineer who is ... and ML/AI-agent stakeholders with well-documented, trustworthy, query-ready datasets and graph ...

Wsgr

Senior AI Risk Advisor

Salt Lake City, UT · On-site +1

... or evaluating AI/ML systems, LLMs, or AI-enabled products is highly valued * Relevant ... inference risks, and applicable regulations such as GDPR and CCPA * Experience evaluating AI ...

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Ml Inference information

What is a $900000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers or AI research directors, often involving advanced skills in deep learning, data modeling, and programming with tools like Python and TensorFlow. These positions usually require extensive experience, specialized knowledge, and may include leadership responsibilities or strategic decision-making.

What is ML inference?

ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.

What is the difference between Ml Inference vs Data Scientist?

AspectML InferenceData Scientist
Required CredentialsKnowledge of machine learning models, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentDeploying models in production, real-time data processingData analysis, model development, research
Industry UsageAI product deployment, software companiesResearch institutions, tech firms, consulting

ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.

What engineer makes $500,000 a year?

Senior machine learning engineers with extensive experience, advanced skills in deep learning, and expertise in deploying large-scale models can earn salaries approaching or exceeding $500,000 annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their specialized knowledge and impact on product development.

Which 3 jobs will survive AI?

Jobs involving Ml Inference, such as data scientists, machine learning engineers, and AI system architects, are likely to persist as they require specialized expertise in developing, deploying, and maintaining AI models. These roles demand critical thinking, domain knowledge, and skills in programming and data analysis that are less easily automated. Continuous learning and staying updated with AI tools and frameworks are essential for these professions to remain relevant.

What are some common challenges faced by ML Inference Engineers when deploying models to production?

ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and optimize AI models and systems. While AI automation tools can assist with certain tasks, MLEs are essential for building, tuning, and maintaining complex models, making complete replacement unlikely in the near term. Their expertise in data handling, model deployment, and system integration remains critical in AI development environments.

What are the key skills and qualifications needed to thrive in ML Inference, and why are they important?

To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.
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Ml Inference jobs near you
Product Security Engineer

Product Security Engineer

NextDeavor Inc.

Lehi, UT • On-site

$67.61 - $84.51/hr

Contractor

Medical, Dental, Vision, Retirement

Posted 9 days ago

Job description

Product Security Engineer
Full-time
Lehi, UT

You’ll be joining Adobe on a contract opportunity, employed through NextDeavor

 
Benefits You'll Love

NextDeavor offers health, vision and dental benefits for contract employees Paid sick leave eligibility is contingent on state of residence Optional 401k Plan (excludes employer match) Opportunity to get your foot in the door at a well-established corporation, with potential for extended or permanent full-time employment

Become a Key Player as a Product Security Engineer

You will lead triage and validation of external vulnerability reports for the client's products, ensuring timely, accurate resolution and clear researcher communications. You will work closely with internal engineering and security stakeholders to drive remediation and improve the bug bounty program's effectiveness. This is a contract engagement expected to backfill a team member on leave.

Here's How You'll Make an Impact on the Team
  • Triage incoming vulnerability reports from the bug bounty platform, assess validity, impact, and scope.
  • Assign CVSS scores and severity ratings following internal guidelines and industry standards.
  • Reproduce proof-of-concept exploits across web, API, and mobile surfaces to validate reports.
  • Communicate with external researchers to request clarifications, provide status updates, and manage expectations.
  • Coordinate confirmed vulnerabilities with product engineering teams for remediation.
  • Identify duplicates, out-of-scope, or informational reports and close them with clear explanations.
  • Contribute to internal documentation, triage runbooks, and severity calibration guidelines.
  • Flag systemic or critical findings to the Bug Bounty team for escalation.
Here's What You'll Need to Be Successful in This Role
  • 3+ years of experience in application security, penetration testing, or a bug bounty/vulnerability disclosure role.
  • Strong understanding of CVSS v3.1 and hands-on experience applying it to real-world vulnerabilities.
  • Proficiency with common web vulnerability classes: XSS, SQL injection, SSRF, IDOR, authentication flaws, and business logic issues.
  • Ability to reproduce and validate PoC exploits using tools such as Burp Suite, browser DevTools, curl, and custom scripts.
  • Familiarity with bug bounty platforms (e.g., HackerOne, Bugcrowd) and responsible disclosure processes.
  • Solid written communication skills for clear, constructive responses to external researchers.
  • Familiarity with attacker techniques against LLM systems and generative AI products.
  • Knowledge of OWASP Top 10 vulnerabilities and mitigation techniques.
Here's What Else Might Help You Out
  • Experience with cloud environments (AWS, Azure, GCP) and API security testing.
  • Hands-on penetration testing experience for AI/ML and LLM-powered products, including chat interfaces and inference APIs.
  • Prior participation in bug bounty programs as a researcher.
  • Familiarity with CWE taxonomy and CVE assignment processes.
  • Background working within a large enterprise or SaaS security organization.
Pay Range

$67.61 - $84.51/hour

Ready to Make Your Mark?

This role may fill quickly. Submit your resume to be considered.

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