Top Machine Learning Jobs

Seeking a Senior Data Scientist/ML Engineer to develop and deploy Machine Learning algorithms, collaborate with teams, and assist federal clients in their digital transformation.

Design, build, deploy, and operate scalable production ML and AI systems (batch and real-time) to predict KPIs and optimize provider network operations. Own end-to-end model lifecycle including feature engineering, training, deployment, monitoring, CI/CD, and experiment tracking. Collaborate with product, engineering, clinical and business stakeholders, implement production-quality code (Python, Spark, SQL), and integrate LLMs, RAG, and other AI capabilities to deliver measurable business and clinical outcomes.

As a Senior ML Engineer, you will develop ML pipelines, work with language models, and build agentic AI systems while implementing MLOps practices.

The Senior Machine Learning Engineer will develop end-to-end ML systems connecting perception inputs to driving behavior, enhance model performance, and mentor juniors.

Develop, deploy, and monitor machine learning and analytic capabilities for defensive cyber operations. Ingest and transform multi-source data, build data pipelines, validate operational models, collaborate with analysts to deliver actionable insights, and document processes for reproducibility in high-security environments.

As a ML Engineer at Wispr, you will design features for a voice interface, optimize ML inference, and scale personalization of speech models.

Design, implement, train, validate, and deploy machine learning models at enterprise scale. Collaborate with data engineers and software engineers to preprocess data, perform feature engineering, evaluate model performance, and integrate models into production. Continuously improve models and adopt new ML and cloud data tools to solve business problems.

The role involves designing scalable distributed systems using Scala, enhancing feature stores for performance, and collaborating with ML engineers to resolve challenges.

Designs, implements, and deploys scalable AI/ML solutions: selects algorithms, engineers features, trains and tunes models, integrates models into production, monitors performance, mentors team members, leads design reviews, and communicates technical strategy to stakeholders.

The Senior AI/ML Engineer will develop and improve analytical tools, approaches, and architecture for scalable AI and machine learning solutions, collaborating with engineers and data scientists on innovative projects.

The role involves developing ML-based planning algorithms for autonomous trucks, ensuring safety checks, real-time operation, and keeping up with research. Requires strong communication and collaboration.

The Machine Learning Performance Engineer will build training and inference pipelines, enhance deep learning frameworks, improve performance, and collaborate with researchers and engineers to optimize trading systems.

Develop, validate, and improve machine learning models (regression, classification, forecasting, optimization). Prepare data, perform feature engineering, train and evaluate models, and support deployment with Data/ML Engineering. Communicate results, document methods, and collaborate on experiments and cross-functional initiatives.

Design, develop, and deploy scalable machine learning models and data pipelines for production. Collaborate with data scientists, engineers, and stakeholders, optimize model performance and scalability, automate deployments, monitor production systems, and evaluate emerging ML and cloud technologies to drive business outcomes.

Work alongside full-time ML researchers on end-to-end projects applying state-of-the-art models to large, nonstationary market datasets. Tasks include data exploration, model development, hyperparameter tuning, debugging training, and analyzing model predictions using large-scale compute and GPU resources. Internship-focused, with heavy emphasis on practical ML implementation and iteration.

Lead design, implementation, and deployment of scalable AI/ML systems for aerospace and defense. Develop RAG, embedding/retrieval pipelines, transformers, GANs, RL and MPC-aligned models. Use GPU/HPC for real-time decisioning, mentor engineers, ensure safety, validation, regulatory and cybersecurity compliance, and support integration and prototyping.

Research and develop ML and statistical algorithms for networking, edge computing, cybersecurity, and autonomy. Build and evaluate simulation-driven solutions, lead subsystem software development, create automated tests, and contribute to proposals and technical plans while presenting results to customers and senior staff.

Lead a small ML engineering pod to turn research into production: design and operationalize training pipelines and low-latency inference services for computer vision, document intelligence, and predictive models. Drive R&D execution, benchmarking, prototyping, migrations from frontier models to fine-tuned/open-source solutions, and mentor early-career engineers while aligning research with product timelines.

Early-career ML engineer builds data pipelines and trains scientific ML models (surrogate and physics-informed), develops visualization/dashboard tools, runs experiments, and hardens prototypes for production while collaborating across engineering teams.

Lead development of internal Python SDKs and agentic AI workflows, build scalable FastAPI ML services, maintain CI/CD with Jenkins/GitLab Runners, and use Galileo for model evaluation and observability to productionize autonomous systems.

Lead end-to-end development of fraud and risk ML systems: define problems, engineer features, build and evaluate models, collaborate with product and engineering, deploy with ML engineers, and monitor production performance to detect drift and trigger retraining.

Drive applied ML research and build data-driven models for LLMs, multi-modal vision, GNNs, NLP, recommender systems and ranking. Write production-quality code, analyze data, scope and independently solve moderately complex problems, and collaborate cross-functionally.

Design, train, test, deploy, and maintain machine learning models for defense applications. Collaborate with data engineers, data scientists, architects, and product owners to implement AI use cases, guide clients on ML algorithms and tools, and drive mission-focused solutions.

Lead end-to-end applied ML: turn problems into datasets, experiments, and production models. Build Python production systems for data collection, feature extraction, scoring, APIs, and observability. Improve LLM systems, handle untrusted data safely, evaluate models and backtests, and work directly with stakeholders to prioritize and iterate.

Lead AI/ML consulting engagements: design and implement ETL, data modeling, predictive models, anomaly detection, distributed data pipelines, and executive-ready visualizations. Drive business development, mentor teams, and deliver client-facing solutions in hybrid/onsite environments.