Top Machine Learning Jobs

Design and implement ML solutions for wireless communications using SDR-captured RF data. Combine signal processing, statistics, and ML to build prototypes and scalable systems from edge devices to cloud, collaborating with UX and infrastructure teams.

Own Coinflow's ML layer: explore transaction and behavioral data, build production models and decisioning systems, establish ML/data practices, define core metrics, and partner with Engineering, Product, and Operations to embed intelligence into products and tooling.

As an AI/ML Data Contributor, you will perform tasks like data labeling and content evaluation, contributing to machine learning model improvements across various projects.

The role involves data labeling, annotation, content evaluation, and testing to enhance machine learning models, requiring attention to detail and basic computer skills.

Assist in improving machine learning models through data labeling, content evaluation, and user-based testing tasks, both remote and on-site.

As an AI/ML Data Contributor, you will assist in data labeling, evaluation, testing, and follow project guidelines while ensuring quality task completion.

Build and deploy ML models and pipelines for entity resolution and a knowledge graph at scale. Fine-tune algorithms, expand data ingestion and enrichment, create evaluation harnesses, and integrate agentic workflows to support research and product teams processing tens of millions of records daily.

Design, develop, deploy, and sustain agentic AI and ML capabilities across the full lifecycle. Build ML models and pipelines, integrate LLMs/agentic workflows, implement CI/CD and containerized deployments, evaluate system safety and performance, and collaborate with engineers and stakeholders to deliver secure, production-ready AI solutions for mission use.

Design, develop, and deploy AI/ML models and full-stack services (LLMs, RAG, vector search) for cybersecurity analyst workflows in secure cloud environments. Integrate systems via APIs, monitor model performance and drift, support retraining, and collaborate with stakeholders to deliver mission-focused solutions under DoD security constraints.

Prototype, develop, and deliver AI/ML solutions; architect and integrate models into products; perform ETL, data curation, and cloud integration (including AWS); work with graph and relational databases and diverse data formats; monitor and tune model performance; collaborate with stakeholders and document processes.

Develop, debug, and maintain AI/ML software solutions; collaborate with engineers and leadership to design systems; build tools for large-scale data analysis; produce documentation and user support materials.

Design, build, and deploy ML models (NLP, time-series, event detection) and LLM-driven systems; develop low-latency model serving, inference pipelines, CI/CD, monitoring, and retraining on AWS; collaborate with data engineers to produce training datasets, feature stores, and scalable production microservices in Python and Go.

Design, implement, and support scalable machine-learning solutions and pipelines on AWS. Collaborate cross-functionally to gather requirements, architect cloud-based ML infrastructure, write production-quality code, troubleshoot production issues, and apply metrics-driven decisions. Follow professional software engineering and DevOps best practices.

Design, train, test, deploy, and maintain machine learning models for DoD-focused solutions. Collaborate with data engineers, data scientists, and product owners to implement ML, deep learning, computer vision, and generative AI use cases while advising clients on algorithms, tools, and deployment.

Design, develop, and deploy production 3D and BEV perception ML models for autonomous trucks. Own end-to-end model lifecycle from data prep, training, evaluation, and deployment. Improve model robustness, performance, and generalization across sensor modalities. Integrate models with autonomy software, enhance training pipelines and tooling, analyze failures, and mentor junior engineers.

Support SMEs to identify, evaluate, and integrate AI/ML capabilities (especially for waveform analysis) into workflows. Provide integration guidance, documentation, training, troubleshooting, and cross-functional collaboration. Support data analysis and build Power BI dashboards, enabling data-driven decisions in a DoD environment. Requires active clearance and stakeholder enablement.

Design, develop, and deploy machine learning models and data pipelines for naval applications. Build and optimize distributed, cloud-native analytics and event-streaming systems, work with multiple data formats and GIS tools, containerize and monitor deployments, apply DoD security practices, and collaborate with multidisciplinary teams to deliver mission-ready AI solutions in shipboard and operational Navy environments.

Design, build, productionize, and maintain large-scale ML models and infrastructure using structured and unstructured data. Perform feature engineering, scale models and systems, run experiments, collaborate with researchers, and ensure code quality through reviews.

Lead design and productionization of scalable ML/LLM systems for legal products. Build LLM applications (RAG, agents), hybrid search, model serving, data pipelines, APIs, and cloud-native infrastructure. Optimize for latency, reliability, and cost; establish deployment/observability best practices; partner with data scientists and provide technical leadership and coaching.

Design, develop, and productionize scalable ML and deep learning models for compliance use-cases using large structured and unstructured data. Build ML infrastructure and feature engineering pipelines, run experiments and tuning, collaborate with researchers, perform code reviews, and deploy models in distributed, big-data environments.

The Senior AI/ML Engineer will design, develop, and deploy scalable ML systems focusing on LLM applications, collaborating with various stakeholders to enhance healthcare data insights.

The Senior Machine Learning Engineer will design evaluation frameworks, develop synthetic data pipelines, build evaluation systems, and collaborate with teams to improve model quality in healthcare AI.

Build and productionize AI systems and infrastructure that power customer-facing features and internal tooling. Partner with product, design, and engineering to identify high-impact ML opportunities, prototype solutions (recommendations, NL interfaces, agents), and manage the full ML lifecycle from data curation to deployment and monitoring.

Design and build custom machine learning and deep learning solutions from first principles. Architect data models and pipelines, write modular maintainable code, implement tests and CI/CD, collaborate with engineers and analysts, and tune/optimize algorithms on on-premise infrastructure.

Lead design, development, and delivery of ML, DL, and Generative AI systems for production. Apply prescriptive analytics, optimization, recommendation, and reinforcement learning. Build and evaluate models, run experiments, develop prototypes, benchmark methods, and collaborate with business partners to translate data into actionable recommendations and influence strategy.