Top Senior Machine Learning Jobs

Lead technical direction for autonomous mineral refining by designing, validating, and deploying RL-based control models. Own autonomy roadmap, set simulator and modeling standards, solve sim-to-real and non-stationarity problems, partner with domain experts and leadership on techno-economic decisions, and mentor engineers while delivering production ML that runs on live plant equipment.

The Senior Python Engineer designs, develops, and deploys machine learning solutions, ensuring scalability and integration into production systems, while collaborating with engineering and data teams.

As a Staff ML Software Engineer, you will lead the development and deployment of machine learning applications, leveraging your expertise to drive software architecture decisions, maintain cloud infrastructure, and collaborate within a small, agile team focused on geospatial challenges related to climate change.

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

Lead ML/AI analytics across STARZ telemetry and operational data to detect anomalies, forecast trends, and surface actionable signals. Build repeatable signals frameworks, apply anomaly detection/classification/clustering/time-series models, leverage generative AI/LLMs for summarization and RCA, define alerting and escalation, partner with engineering and product, and mentor junior analysts.

As an ML Engineer at Luma AI, you will design high-performance model serving pipelines, manage GPU resources, and optimize CI/CD for large-scale AI systems.

The role involves developing advanced machine learning algorithms, solving complex optimization problems using reinforcement learning techniques, and leading technical projects while optimizing solutions for operational efficiency.

Develop AI/ML models for 3D simulation and geology prediction, refine data pipelines, and integrate deep learning tech with product backend.

As a Senior Machine Learning Engineer, you will architect a scalable ML platform, collaborate across teams, and ensure best practices for ML workflows and operations.

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.

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.

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.

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.

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.

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, 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.

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.

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.

Lead development and production deployment of ML products: perform exploratory analysis, build and evaluate models, design experiments and metrics, leverage generative AI and graph-based methods, collaborate with product and engineering, influence strategy, and maintain robust CI/CD and platform practices.