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Ml Inference Jobs in Houston, TX (NOW HIRING)

Socket.dev

Staff AI Product Engineer

Houston, TX · On-site

$220 - $293/hr

Experience building AI/ML product platforms: inference APIs, fine-tuning UX, model management, evaluation tooling * Experience building self-service, paved-path onboarding so teams can ship new ...

Accenture

Senior Knowledge Engineer

Houston, TX · On-site

$99K - $137K/yr

Collaborate with AI/ML engineers to integrate knowledge graphs as grounding and context layers for ... Graph traversal, reasoning, inference, entity resolution, and enrichment Agentic AI & LLM ...

FIRST SERVICE CREDIT UNION

AI Engineer

Houston, TX · On-site

Strong Python skills, including experience with ML/AI frameworks (PyTorch, Hugging Face ... Experience operating self-hosted LLMs (Ollama, vLLM, text-generation-inference) on GPU hardware ...

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Ml Inference information

See Houston, TX salary details

$35.8K

$117.2K

$187.7K

How much do ml inference jobs pay per year?

As of Aug 23, 2026, the average yearly pay for ml inference in Houston, TX is $117,212.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,100.00 and $129,900.00 per year, depending on experience, location, and employer.

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 are the key skills and qualifications needed to thrive in ML inference?

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.

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.

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 are popular job titles related to Ml Inference jobs in Houston, TX?

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What job categories do people searching Ml Inference jobs in Houston, TX look for?

The top searched job categories for Ml Inference jobs in Houston, TX are:

What cities near Houston, TX are hiring for Ml Inference jobs?

Cities near Houston, TX with the most Ml Inference job openings:

Ml Inference jobs near you

Principal Product Manager Talent Acquisition Technology and AI Solutions

HP Development Company, L.P.

Spring, TX • On-site

Full-time

Medical, Dental, Vision, Life, PTO

Re-posted 18 days ago

Job description

Principal Product Manager Talent Acquisition Technology and AI Solutions
Description -
About the Role
HP is transforming the digital experience for candidates, hiring managers, and HR teams worldwide. We are seeking a Product Manager for the Attract & Acquire products with deep expertise across the Talent Acquisition technology landscape and a strong focus on AI-enabled solutions.
This role operates at the intersection of HR, technology, and product management, providing product leadership for Talent Acquisition products associated with the Attract & Acquire value stream. The Principal Product Manager is responsible for defining digital solution strategy, guiding roadmap priorities, and ensuring solutions effectively support global recruiting processes, scalability, and user experience.
This is a strategic IC role for an experienced product leader who brings strong domain judgment, systems thinking, and the ability to influence outcomes across global, cross-functional teams-without formal people management.
What You'll Do
Product Strategy & Platform Ownership
  • Serve as Product Manager for enterprise Talent Acquisition technology platforms, with accountability for product strategy, roadmap definition, and ongoing evolution.
  • Translate Talent Acquisition business needs and priorities into clear product direction and capability roadmaps.
  • Provide product leadership for AI-enabled recruiting capabilities, including talent intelligence, matching, and skills inference, ensuring alignment with recruiting processes and policies.
  • Provide clear direction on priorities, sequencing, and scope to delivery teams, enabling aligned execution against roadmap objectives.
  • Ensure platform decisions consider scalability, user experience, data integrity, and compliance across global regions.

Delivery Enablement & Validation
  • Lead definition of product requirements and capability design, translating business needs into clear, actionable specifications for delivery teams.
  • Own user acceptance testing (UAT) strategy and validation, defining success criteria, guiding test scenarios, and ensuring solutions meet business requirements prior to scaled deployment.
  • Lead and orchestrate proof-of-concept initiatives and pilot programs for new capabilities (e.g., AI-enabled solutions), partnering with delivery teams to validate feasibility, business value, and scalability before broader rollout.
  • Partner with stakeholders to evaluate pilot outcomes and inform roadmap priorities, balancing innovation with operational readiness.

Ecosystem & Integration Alignment
  • Act as the primary product point of contact for the Talent Acquisition technology ecosystem, ensuring alignment across recruiting, core HR, and adjacent talent platforms.
  • Collaborate with architecture, security, and integration partners to ensure solutions align with enterprise standards and approved designs.
  • Provide product input into integration and data flow decisions to support consistent and reliable recruiting data.

Cross-Functional Collaboration & Influence
  • Partner closely with Talent Acquisition leadership and HR Centers of Excellence to ensure platforms effectively support recruiting operations and evolving business needs.
  • Influence cross-functional stakeholders through product expertise, data, and clear articulation of tradeoffs.
  • Participate in vendor roadmap discussions and release planning to align upcoming capabilities with HP recruiting priorities.
  • Serve as a trusted subject matter expert for Talent Acquisition technology within HR and IT communities.

Analytics, Governance & Continuous Improvement
  • Use data and insights to assess platform adoption, performance, and effectiveness, identifying opportunities for continuous improvement.
  • Develop and contribute to governance, change, and adoption practices to support consistent and compliant use of recruiting platforms.
  • Champion responsible application of AI and automation within defined policy and governance frameworks.

Thought Leadership & Knowledge Sharing
  • Maintain deep expertise in Talent Acquisition technology, AI trends, and recruiting best practices.
  • Share knowledge and lessons learned with peers and team members, contributing to capability development across the HR technology community.
  • Stay current on industry trends to inform product enhancements and improvement efforts.

What You Bring
  • 10+ years of experience in HR Technology, HRIS, or SaaS product management roles.
  • Proven success leading complex Talent Acquisition technology initiatives in global enterprise environments.
  • Deep expertise with recruiting platforms such as Eightfold, Workday Recruiting, or comparable ATS/CRM solutions.
  • Familiarity with AI/ML concepts in recruiting, such as skills inference and matching.
  • Strong understanding of end-to-end recruiting processes and how technology enables them.
  • Demonstrated ability to influence without authority, manage competing priorities, and drive alignment.
  • Analytical mindset with experience using data to inform product decisions.

Preferred Qualifications
  • Experience with Workday, Eightfold, ServiceNow, or similar enterprise HR platforms.
  • Background in HR data, reporting, or analytics.
  • Participation in HR technology forums or product communities.

The pay range for this role is $116,150 to $205,200 USD annually with additional opportunities for pay in the form of bonus and/or equity (applies to United States of America candidates only). Pay varies by work location, job-related knowledge, skills, and experience.
Benefits:
Health insurance
  • Dental insurance
  • Vision insurance
  • Long term/short term disability insurance
  • Employee assistance program
  • Flexible spending account
  • Life insurance

Generous time off policies, including:
  • 4-12 weeks fully paid parental leave based on tenure
  • 11 paid holidays
  • Additional flexible paid vacation and sick leave (US benefits overview)

The compensation and benefits information is accurate as of the date of this posting. The Company reserves the right to modify this information at any time, with or without notice, subject to applicable law.
Job -
Human Resources
Schedule -
Full time
Shift -
No shift premium (United States of America)
Travel -
Relocation -
Equal Opportunity Employer (EEO) -
HP, Inc. provides equal employment opportunity to all employees and prospective employees, without regard to race, color, religion, sex, national origin, ancestry, citizenship, sexual orientation, age, disability, or status as a protected veteran, marital status, familial status, physical or mental disability, medical condition, pregnancy, genetic predisposition or carrier status, uniformed service status, political affiliation or any other characteristic protected by applicable national, federal, state, and local law(s).
Please be assured that you will not be subject to any adverse treatment if you choose to disclose the information requested. This information is provided voluntarily. The information obtained will be kept in strict confidence.
For more information, review HP's EEO Policy or read about your rights as an applicant under the law here: "Know Your Rights: Workplace Discrimination is Illegal"

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