Top Machine Learning Jobs

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.

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 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 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.

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.

Lead AI/ML consulting engagements, build and deploy machine learning models and ETL pipelines, analyze large datasets, design data architectures, deliver visualizations, drive business development, and manage client-facing teams in high-stakes situations.

The Staff ML Engineer will lead the architecture and development of AI/ML features, oversee complex systems, mentor teams, and enhance product experiences through ML solutions.

Develop and improve machine learning models and infrastructure for humanoid robot manipulation. Monitor and evaluate model performance on real robots, and collaborate on the ML software stack supporting manipulation, navigation, locomotion, and perception.

Lead design and productionization of next-generation search ranking and recommendation systems using transformers, LLMs, GNNs, and deep learning. Build personalized, real-time retrieval and ranking models, collaborate cross-functionally to run experiments and A/B tests, and mentor engineers on MLOps and model development to improve retailer discovery and search relevance.

Serve as the liaison between mission stakeholders and ML teams: elicit and document requirements, translate mission problems into user stories and technical specs, define model acceptance criteria, support UAT, maintain the MaaS product backlog, and communicate AI/ML concepts across technical and non-technical teams to ensure mission-relevant outcomes.

Design, build, evaluate, and deploy predictive ML models across regression, classification, NLP, computer vision, and time-series problems. Perform data cleaning, feature engineering, EDA, model selection, tuning, explanation (SHAP, feature importance), and collaborate with engineers and stakeholders to operationalize and monitor models in production.

Build and deploy large-scale ML systems for search, ranking, and personalization. Process billions of events and petabytes of logs into consumable features, train transformer and embedding models, serve models in real time, track impact metrics, and collaborate cross-functionally while mentoring engineers.

Design, deploy, and maintain large-scale ML workflows and knowledge graph services. Build production-ready LLM and RAG-based search pipelines, data ingestion/preprocessing, feature stores, monitoring, and security. Collaborate with cross-functional teams, document systems, research advanced ML/NLP techniques, optimize knowledge graph algorithms, and mentor junior engineers.

Design, build, and productionize personalized recommendation models and LLM-based experiences for Spotify's Home feed. Own model training, fine-tuning, evaluation, A/B testing, inference optimization, and ML platform/data pipeline improvements. Drive technical direction, mentor engineers, and collaborate with cross-functional partners to deliver scalable, cost-efficient personalization at global scale.