Top Remote Machine Learning Jobs in Huntsville, AL

The role involves developing and deploying machine learning solutions, optimizing models, and collaborating with cross-functional teams. Responsibilities include cloud architecture, algorithm engineering, and infrastructure best practices.

Own end-to-end production ML systems for search, recommendations, and ranking: architecture, training pipelines, inference, deployment, monitoring, drift detection, and retraining. Partner with data, product, and backend teams to ship reliable, low-latency ML-driven product experiences and set standards for ML infrastructure and model health.

Lead and grow a small ML engineering team while staying hands-on: design and ship GenAI features, build agentic LLM systems (NER, classification, retrieval, summarization), partner with PM and engineering, scope ambiguous problems, and reduce COGS through ML automation.

Build and validate production-grade ML and AI systems across pricing, underwriting, claims, and portfolio management. Work with structured and unstructured data, LLMs and agentic workflows, extract information from documents, resolve entities, create feature pipelines and inference services, quantify uncertainty, monitor drift, and measure business impact in collaboration with engineers, actuaries, underwriters, and product teams.

Lead and grow a team of ML engineers building production ML systems for Fetch's Ad Platform. Translate product strategy into roadmaps, guide design and implementation of models for ranking, targeting, bidding and forecasting, set metrics and experimentation, enforce architectural and operational standards, manage trade-offs across performance and reliability, and drive cross-functional alignment to improve advertiser and member outcomes.

Lead design, deployment, and monitoring of predictive models for credit risk, collections, and operations. Drive adoption of modern ML and MLOps, mentor data scientists, define strategic roadmap, advise senior leadership, and ensure model governance and regulatory compliance.

Design, build, and ship AI-powered observability features using LLMs and agentic workflows. Rapidly prototype, iterate with real users, integrate AI into alerting, runbooks, and developer workflows, and ensure solutions are scalable, maintainable, and production-ready while collaborating cross-functionally.

Design and implement scalable ML solutions for customer experience problems. Analyze customer data, research and deploy ML/NLP models, automate model development and validation, and produce performance analyses.

The Machine Learning Engineer will execute the full ML lifecycle, collaborating on model implementation and improving customer experiences through data-driven insights.

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

As a Machine Learning Engineer, you'll develop and deploy ML models for medical imaging, collaborate with teams, and optimize for production environments.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

Lead the strategy and implementation of AI solutions in R&D, driving adoption and improving operational efficiency through advanced analytics. Collaborate with cross-functional teams to design AI/ML models and evaluate new technologies.

Lead research on humanistic AI for autonomous systems. Collaborate with teams, design experiments, and publish findings in top-tier publications.

As a Lead Machine Learning Engineer at Root, you'll build scalable ML systems, partner with data scientists, and improve operational practices for customer lifetime value modeling.

Lead technical execution across backend, frontend, mobile, AI, and data. Translate ambiguous product goals into technical plans, unblock hard engineering problems, coordinate cross-domain work, raise engineering quality, define hiring needs and technical evaluation standards, and build internal AI workflows to foster an AI-native engineering culture.

As a Fraud ML Engineer, you will ensure authentic human data is processed by building detection systems using adversarial ML techniques and managing backend systems.

The AI/ML Engineering Manager leads a team of ML engineers and architects, managing hiring, performance, and customer engagements while providing technical guidance in ML operations and architecture.

The role involves leading ML client engagements, shaping strategies, advising on ML architecture, and influencing project outcomes, while guiding teams and driving business value.

The Junior AI/ML Engineer will deliver machine learning projects, handle Python programming, and learn from a senior team member, focusing on real impact initiatives.

As an AI/ML Presales Solutions Architect, you will engage with clients to identify problems, create feasible AI solutions, and partner with sales to ensure project delivery aligns with capabilities. You will leverage your extensive experience in AI and cloud platforms to shape presentations and technical discussions with clients.

Design and improve ML training frameworks, modularize experiments, adapt fire-hazard detection algorithms for varied electrical environments, build scalable pipelines for high-frequency sensor data, and develop dashboards and tooling to monitor model performance and drift while collaborating across product, data science, and fire safety teams.

As an AI/ML Architect, you will define scalable AI systems architecture, mentor teams, guide clients, and ensure ethical AI practices.

As a Senior ML Engineer, you'll own ML-driven features from modeling to deployment, drive innovation in generative AI, and mentor junior members.

Lead Android-first development of growth experiments and features, using AI-native workflows to design, implement, instrument, and analyze A/B tests. Ship performant, reliable Android experiences at scale, mentor teammates, participate in on-call, and codify AI-native engineering playbooks for broader adoption.