Top Machine Learning Jobs in Huntsville, AL

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

Develop and deploy machine learning models for fraud detection and credit risk decisioning. Own the full ML lifecycle, including data exploration, feature engineering, experimentation, validation, deployment, monitoring, and optimization. Analyze fraud trends and portfolio outcomes, collaborate with Product, Engineering, and Risk Operations, apply generative AI tools, and mentor fellow scientists while delivering measurable improvements in fraud prevention and responsible credit access.

Own the complete machine learning model lifecycle, from experimentation and validation through deployment, monitoring, retraining, rollback, and retirement. Build reusable training, evaluation, deployment, orchestration, and CI/CD infrastructure. Establish model promotion standards, versioning, lineage, monitoring, alerting, and incident-response processes. Partner with data scientists to preserve scientific validity, improve reliability and efficiency, support delayed-label use cases, mentor teammates, and define MLOps priorities.

Own the end-to-end computer vision platform for fine-grained item identification and pricing. Responsibilities include designing and fine-tuning vision models, defining evaluation and confidence standards, building labeling feedback loops, prioritizing coverage versus accuracy, and deploying reliable, low-latency serving infrastructure at scale. The role also communicates model capabilities and trade-offs to technical and non-technical stakeholders.

Build and operate distributed GPU training platforms for foundation models. Run and debug multi-node PyTorch and Ray Train workloads, tune GPU networking and scheduling, profile performance, and develop benchmarks and evaluation harnesses. Partner with infrastructure teams and client scientists to improve throughput, reliability, cost efficiency, observability, and end-to-end training pipelines on GKE.

Design, train, and deploy ML models for Qur'ān recitation, tajwīd detection, and ASR. Build data pipelines for cleaning, segmentation, labeling, and evaluation. Integrate real-time predictions via APIs or lightweight on-device inference, optimize models for speed and mobile performance, and collaborate with backend developers to ship AI features in the app.

Develop, test, and integrate AI software for aerospace and industrial applications. Interns will use Python, JavaScript, and C++ to build AI solutions, evaluate data and algorithms, collaborate with machine learning and data engineers, monitor AI trends and regulations, and present progress to leadership and stakeholders.

Design and lead the technical strategy for search across Vannevar’s products. Build and improve full-text, vector, and LLM-powered retrieval systems, optimizing latency, relevance, index quality, freshness, and performance. Collaborate with ML, product, design, and mission strategy teams to translate user needs into search innovations. Apply expertise in information retrieval, NLP, machine learning, indexing, ranking, query parsing, tokenization, vector search, and evaluation frameworks.

Lead research on improving AI agent training data and evaluations. Manage and mentor research engineers, define research direction, design rigorous experiments, validate data quality, diagnose reward and grading failures, and translate findings into scalable tools, workflows, and standards. Collaborate with engineers, domain experts, and vendors while communicating technical conclusions and tradeoffs clearly.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

Build and evaluate machine learning models and features for fraud detection across account opening, account takeover, and identity risk. Analyze complex datasets to identify fraud patterns, develop hypotheses, validate models, and measure technical and business performance. Write tested Python code, support production deployment and monitoring, communicate findings, and follow standards for privacy, explainability, validation, documentation, and governance.

Owns P&L performance, profitability, growth, and digital transformation for Small and Lower Mid-Market commercial insurance portfolios. Leads pricing, underwriting, portfolio monitoring, analytics, data visualization, predictive modeling, and machine learning initiatives. Partners with actuarial, product, engineering, technology, and distribution teams to implement process improvements and underwriting actions across digital channels and geographies. Communicates insights to stakeholders and senior leadership while leading cross-functional teams in an Agile operating model.

Serve as the persistent technical partner for strategic and premium customers, diagnosing workflows, labeling, configurations, model behavior, and technical health. Build success plans with Customer Success Managers, guide implementations and model releases, conduct technical health reviews, coordinate fraud incident responses, and communicate technical findings to business and engineering stakeholders. Identify recurring customer patterns and provide feedback to Product and Engineering while supporting account retention and growth.

Own technical relationships with insurance carrier accounts by leading discovery, solution design, integrations, deployment support, defect management, QA, documentation, API and payload validation, SQL-based account monitoring, and incident management. The role partners with client engagement and engineering teams to ensure deployments meet carrier requirements and progressively assumes greater ownership of technical account relationships.

Provide technical pre-sales expertise for Jumio’s identity verification, KYC, AML, authentication, and risk management solutions. Responsibilities include consulting with prospects, leading technical presentations, responding to RFPs and RFIs, managing proofs of concept, onboarding customers through go-live, delivering customized web and mobile demos, and supporting sales, account management, product, engineering, and marketing teams.

Lead and grow a team of Conversation Designers to build and scale conversational AI experiences. Partner with Product, Engineering, Analytics, and ML teams to drive strategy, tooling, evaluation, and quality practices. Recruit and remove roadblocks, own strategic initiatives, and remain hands-on in designing, testing, and improving AI-driven conversational products.

Leads strategy, roadmap, execution, commercialization, and customer outcomes for a major enterprise software product area. Conducts market and customer discovery, prioritizes opportunities, partners with engineering and design, establishes outcome-based metrics, and supports sales enablement. Builds and coaches the product management team, manages executive and customer relationships, and helps transition the company from services-led to product-led operations. The role emphasizes legal technology, eDiscovery, AI-enabled workflows, enterprise data, and regulated-industry requirements.

Designs and implements enterprise-scale AI and machine learning architectures and applications to solve complex client problems. Develops scalable software, processes structured and unstructured data, operationalizes ML platforms, and creates innovative solutions with minimal supervision. Collaborates cross-functionally, supports platform monitoring and operations, and mentors employees. The role requires TS/SCI clearance and may involve full-stack development, model deployment, distributed computing, streaming data, and data warehousing.

Develops, trains, tests, deploys, and maintains production computer vision and machine learning models for mission-critical applications. Responsibilities include deep learning, transformer-based vision models, sensor fusion, target tracking, GPU optimization, embedded systems, and real-time missile defense software. The engineer collaborates with multidisciplinary space, military, intelligence, engineering, and AI teams to design and implement robust operational capabilities.

Develop machine learning, AI, statistical, and predictive models using Snowflake and other data sources. Perform data extraction, cleansing, feature engineering, experimentation, visualization, and quality analysis. Partner with business and technical teams to deliver data products, conversational analytics, and actionable recommendations. Lead discovery sessions, communicate insights to nontechnical audiences, mentor analysts, and support enterprise data science projects.

Own technical delivery and account health for insurer software deployments. Responsibilities include discovery, solution design, integrations, API and payload review, QA testing, defect documentation, workflow mapping, SQL-based production monitoring, incident management, and technical communication with carrier accounts and engineering teams.

Owns customer retention and expansion across a portfolio of accounts. The Renewals Manager engages customers before renewal, drives value-based conversations, analyzes usage and health data, identifies churn and expansion opportunities, and coordinates renewal execution across Sales, Finance, and Legal. The role also develops scalable engagement frameworks, presents pipeline insights to leadership, and partners with Account Executives and technical customer success teams in a B2B technology environment.

Lead hands-on engineering for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Define APIs and abstractions, make architecture and scalability decisions, improve observability and reliability, and lead a small team of senior engineers through technical reviews, mentorship, and production delivery.

Analyze large datasets using data science, machine learning, predictive modeling, and big data technologies. Design approaches for data handling, search, retention, storage, and processing; develop software to improve data searches; validate data quality and processes; and report actionable findings. The role emphasizes scientific data work over visualization and requires an active Top Secret/SCI-eligible clearance with potential CI polygraph eligibility.