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Ml Inference Jobs in Houston, TX (NOW HIRING)

Hexagon AB

Principal AI/ML Software Engineer

Houston, TX · On-site

$124K - $167K/yr

Strong foundation in statistics, A/B testing, causal inference, and experimental design • ... ML engineering, or related roles • 3+ years building NLP/generative AI applications and ...

Hp

AI/ML Platform Engineer

Spring, TX

$147K - $230K/yr

Helping teams productionize models-hosting options, inference patterns, scaling, cost, and operational readiness across SageMaker, Bedrock, Azure ML/AI Foundry, and Kubernetes Docs & platform ...

New

Recru

Data Scientist

Houston, TX

Experience building and deploying production ML systems in cloud environments (AWS, Azure, or GCP). * Strong understanding of: * Time-series analytics, Statistical inference, Feature engineering ...

Augment

Principal Scientist, Data

Houston, TX

$80 - $83/hr

Build LLM-powered solutions using prompt engineering, fine-tuning, inference optimization, and agent-based architectures. Business Decision Support * Convert statistical and ML findings into ...

Cisco

Applied AI Scientist

Houston, TX

Explore and evaluate new AI/ML techniques, tools, and methodologies, applying relevant innovations ... and inference efficiency to minimize cost and latency while preserving accuracy. * MLOps ...

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All JobsMl Inference JobsMl Inference Jobs in Houston, TX

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 Jul 18, 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 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.
What are popular job titles related to Ml Inference jobs in Houston, TX? For Ml Inference jobs in Houston, TX, the most frequently searched job titles are:
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 AI/ML Software Engineer

Principal AI/ML Software Engineer

Hexagon AB

Houston, TX • On-site

$124K - $167K/yr

Full-time

Posted 8 days ago

Job description

Principal AI/ML Software Engineer
Job Location (Short): Houston, Texas-USA | Madison, Alabama-USA
Workplace Type: Remote
Req Id: 2909
Responsibilities
Position Overview
We are seeking a motivated AI/ML Engineer to build reliable, scalable systems and Generative AI and Agentic AI features, and build and deploy data-driven solutions for our document-based compliance management platform. This role requires a technical expert who can develop, deploy, and maintain ML systems in production environments.
Key Responsibilities
• Build and deploy Generative AI features using foundation models (AWS Bedrock, OpenAI, Anthropic Claude) and inference pipelines with optimization of latency and cost
• Design agentic AI systems that autonomously handle compliance workflows, document review, regulatory mapping, and multi-step reasoning tasks
• Integrate comprehensive LLM evaluation frameworks with development and production systems
• Build and operate end-to-end MLOps pipelines, deployment systems, monitoring, and rollbacks workflows
• Implement explainability frameworks (SHAP/LIME) and monitoring dashboards ensuring transparency and regulatory adherence
• Collaborate with cross-functional teams to translate business needs into ML solutions and communicate insights to stakeholders
#LI-PB1 LI-Remote
Education / Qualifications
Technical Skills
• Python (5+ years): Production-level experience with Pandas, NumPy, scikit-learn, XGBoost, TensorFlow/PyTorch, Hugging Face Transformers, FastAPI/Flask, MLflow, and pytest
• SQL: Advanced proficiency with complex queries, window functions, and optimization
• Machine Learning & NLP: Strong foundation in supervised/unsupervised learning, deep learning, document understanding, text classification, and semantic analysis
• Generative AI & LLMs: Hands-on experience with foundation models (GPT, Claude, Llama), prompt engineering, RAG architectures, and vector databases (Pinecone, Weaviate, Chroma)
• MLOps & ModelOps: End-to-end experience with ML pipelines, model versioning, feature stores, drift detection, CI/CD for ML, and Docker containerization
• LLM Evaluation: Experience with evaluation frameworks (RAGAS, DeepEval), custom metrics, benchmark datasets, and human-in-the-loop validation
• Cloud & AWS: Experience with AWS services including SageMaker, Bedrock, S3, Lambda, EC2, and CloudWatch
• Statistics & Experimentation: Strong foundation in statistics, A/B testing, causal inference, and experimental design
• Visualization: Proficiency with Tableau, Power BI, or Python visualization libraries
Experience & Education
• 5+ years in data science, ML engineering, or related roles
• 3+ years building NLP/generative AI applications and implementing MLOps in production
• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or related field
• Track record of deploying ML systems processing large-scale datasets with proper monitoring and governance
Preferred Qualifications
• Experience with agentic AI frameworks (LangGraph, LangChain, AutoGen, CrewAI) ?
• Knowledge of Life Sciences/regulated industries (FDA, EMA, ISO, GxP) and compliance management systems
• Familiarity with big data tools (Spark, Databricks, Snowflake), orchestration (Airflow, Kubeflow), and monitoring tools (Datadog, Prometheus)
• Experience with LLM fine-tuning, document processing libraries, multi-modal AI, or distributed training
• Understanding of ML governance, bias detection, model risk management, and data privacy regulations (GDPR, CCPA, HIPAA)
• Experience working in agile environments with Jira
• AWS ML certifications or similar credentials
Key Competencies
• Strong communication skills explaining complex models to technical and nontechnical audiences
• Ability to work independently and collaboratively in fast-paced environments
• Proven ability to convert POCs into production-grade solutions
• Understanding of ethical AI and building trustworthy, explainable systems for regulated environments
What You'll Build
• LLM evaluation frameworks ensuring 95%+ accuracy for compliance-critical features
• Prompts for LLMs to achieve specific, high-quality outcomes
• Agentic AI systems autonomously handling document review and compliance workflows
• GenAI document understanding features processing millions of regulatory documents
• Predictive models identifying compliance risks before they occur
• Real-time semantic search and explainable ML systems meeting regulatory requirements
• Production MLOps pipelines supporting dozens of models with automated monitoring and retraining
Growth Opportunities
• Drive adoption of emerging AI technologies and establish best practices
• Mentor ML engineers
• Shape AI/ML roadmap and establish center of excellence for compliance AI
• Collaborate with product leadership on long-term vision for AI-powered compliance
About Octave
Octave provides mission-critical software that empowers organizations to make informed decisions across every stage of the asset lifecycle - Design, Build, Operate and Protect - where performance, safety, and reliability are non-negotiable and failure is not an option.
Turning complex operational data into actionable intelligence, Octave connects expertise, real-world conditions and enterprise-scale insight to improve performance, resilience and incident response where it matters most.
Octave has more than 7,000 employees in 45 countries. Learn more at octave.com and follow us on LinkedIn.
Why work for Octave?
All in. Always forward. That's the way we do things around here. We put trust in our people because we believe it's the best way to unleash potential, bring ideas to life, and keep moving ahead. And it's why we're committed to creating an environment that's truly supportive, providing you with the resources you need to support your ambitions, no matter who you are or where you are in the world.
Everyone is welcome
At Octave, we believe that diverse and inclusive teams are critical to the success of our people and our business. Here, everyone is welcome. As an inclusive workplace, we don't discriminate. In fact, we embrace differences and are fully committed to creating equal opportunities, an inclusive environment, and fairness for all.
Respect is the cornerstone of how we operate, so speak up and be yourself. You're valued here.
Recruitment Fraud Alert
Octave posts all official job opportunities on either https://careers.octave.com/ or https://www.octave.com/about/careers and communicates only from email addresses ending in @octave.com. We never request payment or personal banking information during recruitment. No offers will ever be extended without a proper interview via Teams or in person, never done over email alone. If you suspect fraud, it probably is, and contact us at careers@octave.com

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