Top Senior Machine Learning Jobs in Birmingham, AL

The role involves deploying and supporting CrowdStrike's AI capabilities in cybersecurity, optimizing threat detection, and mentoring teams on AI solutions.

Manage a team of ML engineers to build and optimize Affirm's economic decisioning engine using advanced ML techniques. Collaborate across teams, provide technical leadership, and mentor engineers while driving a cohesive long-term ML strategy.

As an AI Engineering Manager at PwC, you will lead the design and operation of AI-powered platforms, mentor engineers, and ensure project delivery excellence while focusing on security and scalability.

Lead the In-Vehicle Experience team at Samsara, focusing on transforming in-cab cameras into intelligent companions. Collaborate with engineering, prioritize features, and ensure client interactions highlight trust and connection. Conduct customer research and integrate AI and ML into product features.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

This role involves applying advanced data science and machine learning methods to healthcare data, leading teams, managing client relations, and developing analytics products for payers, while mentoring junior staff.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

The Enterprise Account Executive will manage the complete sales cycle, build relationships with senior leaders, and drive platform adoption within data-intensive organizations.

Lead Pluralsight's curriculum strategy to identify and shape future skills; translate market and technology trends into a curriculum roadmap; align with product, GTM, and content production; measure portfolio performance with data; lead and develop curriculum directors and managers; define scalable frameworks and represent the company externally.

The Engineering Manager, Data Science will lead a team to drive machine learning initiatives in AdTech, focusing on optimization and modeling strategies to enhance client success and operational efficiency.

Lead technical direction and delivery of ML platform infrastructure (feature store, model serving, experimentation, training pipelines). Drive architecture, resolve system-level issues, write production code, mentor engineers, and partner with ML, data, and product teams to enable scalable, reliable ML model development and deployment.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

The Senior Software Engineer will design and optimize recommendation algorithms, maintain data pipelines, and improve personalized user experiences using machine learning techniques and backend engineering skills.

As a Senior Staff Machine Learning Engineer at Affirm, you will lead complex ML system design and implementation, mentor engineers, and drive innovative ML strategies for financial services.

As a Principal Software Engineer, you'll architect and code backend services and ML models, integrate them with Salesforce, and mentor junior engineers.

Design and implement data-centric cybersecurity solutions: develop ML algorithms and automation for security tools; discover, parse, map, and transform legacy RMF artifacts into OSCAL; migrate and analyze security data; reproduce dashboards/workflows in eGRC; collaborate with Professional Services; produce migration assessments and reports.

Lead strategy and execution for AI-powered nutrition and coaching features. Define product roadmap, partner with engineering/data/design, run experiments and evaluations, ship 0-to-1 ML products, and ensure responsible AI practices and measurable user impact.

Lead product development for Socure's core fraud platform: build reusable services, APIs, and ML-powered features (graph, signals, model lifecycle). Drive prioritization, cross-functional delivery, KPI definition, customer discovery, and integration to improve detection accuracy and operational performance across fraud products.

Lead BI analytics and governance for client accounts: manage BI analysts, deliver dashboards and reports, oversee data mapping, modeling, quality, and platform initiatives (Power BI/Databricks), advise stakeholders, drive analytics roadmap, and apply advanced analytics and ML to optimize workplace and portfolio insights.

Lead and scale quantitative trading and liquidity strategies for NEAR Intents. Design game-theoretic models, build signal generation and execution systems, develop research and simulation infrastructure, translate research into production, monitor performance, and hire and mentor a quantitative research team while partnering with protocol and product teams.

Design, build, and operate production-grade AI systems using ML, LLMs, RAG, and agentic workflows. Define evaluation strategies, implement data/retrieval/orchestration layers, apply MLOps/LLMOps, and partner with stakeholders to prioritize, monitor, troubleshoot, and improve AI solutions balancing quality, latency, cost, reliability, and maintainability.

Lead the integration of AI and digital solutions in manufacturing processes, driving adoption and measurable impact on efficiency, quality, and performance.

Build and scale core components of an end-to-end proteomics software stack (LIMS, data pipelines, web portal). Design agentic backends and AI-augmented tooling, develop analysis algorithms and lab integrations, deploy cloud IaC and CI/CD, write tests and monitoring, and collaborate with R&D, operations, and customers to productionize tools for high-throughput proteomics.

Develop and scale machine learning training systems for robotics data, optimize training pipelines, and collaborate with ML researchers to enhance autonomy models.