Top Entry Level Machine Learning Jobs in Houston, TX

Develop high-performance research platforms and tools that bring trading strategies to life. Work in small teams to design, build, and deploy resilient systems using distributed computing, NLP, and machine learning to solve large-data problems in finance.

Supports CrowdStrike Falcon Intel Recon customers by analyzing and triaging cyber threat intelligence, explaining dark web findings, managing customer relationships, escalating noteworthy intelligence, and communicating feedback to Intelligence and Engineering teams. The role requires knowledge of threat intelligence lifecycles, dark web ecosystems, enterprise security, customer engagement, AI-enabled workflows, and presenting technical information to varied audiences. Travel is minimal to light, up to 25%.

Graduate role in bp’s three-year One Digital program, rotating across Data & AI and Digital Engineering. Responsibilities may include building cloud solutions, developing applications and APIs, analyzing data, creating machine learning and AI solutions, supporting testing and reliability practices, and collaborating with technical and business teams. The role is hybrid in Houston, includes relocation assistance, and requires strong programming, analytical, communication, and problem-solving capabilities.

Drive growth of HP Z high-performance workstations in the AI PC market, emphasizing AMD platforms. Engage strategic customers and partners, support qualification and sales, develop customized solutions, advise on proofs of concept and training, create technical collateral and TCO models, share customer requirements with product teams, and lead industry-focused sales efforts. The role requires strong technical, business, communication, presentation, and relationship-building skills, with more than 50% domestic travel.

SLB is hiring graduates for early-career digital technology roles across software engineering, AI, data, embedded systems, high-performance computing, IIoT, quality assurance, security, scientific computing, and UX design. Employees will apply cloud, data, HPC, and connected-device technologies to solve complex challenges and support digital transformation. The program offers training, professional development, and opportunities to collaborate across SLB’s global technology network.

Conduct research at the intersection of AI, cybersecurity, and computer systems. Formulate research questions, design experiments, build prototypes, evaluate AI and security methods, analyze real-world security data, and communicate findings through reports, presentations, and potential publications. The role combines research with practical engineering and offers mentorship while developing independence.

Deploy and customize Palantir Foundry and AIP solutions for enterprise clients. Responsibilities include conducting client workshops, building data pipelines and operational workflows with Python and TypeScript, solving complex technical problems, training end users, documenting solutions, and supporting project delivery. The role requires strong data engineering, communication, analytical, and problem-solving skills, along with recent Palantir experience and approximately 5% travel in Canada and the United States.

Develop reproducible emissions calculations and analytical methodologies using Python, SQL, and PySpark. Investigate complex energy and emissions datasets, identify data-quality issues, apply statistics and machine learning, and translate research into scalable production capabilities. Collaborate with engineering, product, research, and customer teams while contributing maintainable code through Git, testing, documentation, and code review.

Own research and implementation of post-training methods for legal language models, including supervised fine-tuning, reinforcement learning, reward design, verifiers, preference modeling, evaluations, and distributed training. Lead experiments from research direction through production deployment, while debugging training instability, reward hacking, and divergence. Build rigorous evaluation systems and production-quality Python code for long-horizon legal reasoning and agentic workflows.

Build synthetic data pipelines, evaluation systems, benchmarks, verifiers, rubrics, and quality metrics for legal AI models and agentic workflows. Partner with researchers, legal experts, and customers to investigate model failures, validate metrics, maintain data quality and provenance, and convert expert feedback and product usage into training signals and improved evaluations.

The Director, AI Sales will grow Databricks’ enterprise AI business by driving strategic account adoption, production usage, bookings, and consumption. Responsibilities include developing AI sales plays, enabling field teams, securing executive and technical sponsorship, advancing complex opportunities, coordinating sales and partner stakeholders, and translating customer feedback into product and go-to-market recommendations.

Conduct empirical AI safety research with mentor guidance, owning projects from hypotheses and controlled experiments through analysis and technical writing. Build and debug evaluation harnesses, agent environments, datasets, monitoring methods, and post-training experiments. Investigate model failures, alignment, deception, misuse, multi-agent risks, and evaluation reliability. Produce reproducible code, experiment logs, research analyses, and papers for potential submission to leading machine learning conferences.

Develop and support software, automation tools, dashboards, and alarms for cloud infrastructure, cost attribution, and cloud efficiency. Collaborate with cross-functional teams and senior engineers to design reliable systems, APIs, databases, tests, and AI agents. Contribute to architecture and automation strategies while maintaining strong security, communication, and delivery practices.

Conduct independent AI research spanning large language models, reasoning, knowledge acquisition, factuality evaluation, and trustworthy AI. Translate research ideas into scalable prototypes, implement and extend state-of-the-art methods, design experiments, develop algorithms and evaluation methods, use AI-assisted development tools, and communicate findings through technical reports and research papers.

Conduct coding-heavy machine learning research involving model development, training pipelines, inference systems, numerical methods, performance optimization, and validation. Build reproducible workflows in Python and command-line tools, implement model and data pipeline components, debug system failures, and verify correctness and performance. The contract is fully remote, flexible, and approximately 15 hours per week, with output-based compensation.

Develop and refine machine learning models using Python, analyze datasets with MongoDB, and integrate preprocessing and data pipelines. Evaluate models, tune hyperparameters, benchmark performance, document experiments, and deliver data-driven recommendations. Collaborate remotely with cross-functional contributors to improve and operationalize machine learning solutions in cloud or enterprise environments.

Build production-ready agentic AI capabilities focused on monitoring, guardrails, evaluation, security, quality, and human-agent collaboration. Perform machine learning tasks including exploratory data analysis, feature engineering, and model fine-tuning. Translate research into scalable software, rapidly prototype features, collaborate cross-functionally, and explore emerging agentic technologies.

Leads AI governance and assurance engagements, designing policies, controls, operating models, risk assessments, and compliance programs. Evaluates AI security, model governance, third-party solutions, and control effectiveness against frameworks including NIST AI RMF, ISO 42001, and the EU AI Act. Collaborates with security and engineering teams to test threats such as prompt injection, model manipulation, data poisoning, and leakage. Advises executives and clients, develops methodologies and proposals, researches emerging AI risks and regulations, and supports business development.

Review and annotate robotic-system video footage by identifying objects, movements, events, visual features, and anomalies. Provide feedback to improve video datasets and annotation consistency, collaborate with AI teams on annotation protocols, document observations, participate in quality reviews, and recommend improvements to video capture and labeling strategies.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Design and deploy production-ready LLM pipelines and agentic workflows, automate large-scale multimodal dataset pipelines, monitor and improve agent performance, and integrate AI agents with 3D scene composition. The role also involves process documentation, incident response, and continuous improvement, with collaboration across the engine team and company founders.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Paid, part-time Creative Advisor collaborating with AI research and machine learning teams. The role involves sharing creative expertise, defining quality standards, developing work, reviewing model outputs, identifying limitations, and helping fellow creators engage with the program. Applicants should be leading creators with strong portfolios and interest in shaping AI’s impact on creative practice; no AI experience is required.

Applies statistics, experimentation, data analysis, and machine learning to complex datasets and AI model evaluation. Designs analyses, develops and validates models, performs Python and Pandas data transformations, creates expert prompts and datasets, identifies quantitative errors, and provides structured feedback. Produces reproducible code, analytical reports, and clear documentation while collaborating with stakeholders and reviewing peer contributions.

Own product outcomes for an AI-native cloud infrastructure platform. Conduct customer discovery, frame problems, prioritize opportunities, prototype solutions, analyze product and business data, and collaborate with Design, Engineering, Sales, Customer Success, and Marketing. The role requires enterprise B2B product judgment, technical fluency across cloud infrastructure and FinOps, strong commercial awareness, AI-assisted workflows, outcome measurement, and end-to-end ownership from strategy through iteration.