Top Senior Machine Learning Jobs

Design, develop, and deploy end-to-end machine learning models using statistical and ML algorithms. Prepare and enrich data, implement scalable PySpark/Databricks solutions, and collaborate with product and engineering to create personalized customer experiences and actionable insights.

Build and productionize distributed pre-training stacks (PyTorch/JAX) at multi-node GPU scale, implement new optimizers and high-performance CUDA/Triton kernels, optimize mixed-precision training and memory/communication, develop experiment infrastructure and monitoring, and mentor junior engineers to accelerate foundational model research.

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.

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.

Lead technical development of machine learning algorithms for the onboard Planner, advancing imitation learning, reinforcement learning, and model scaling; integrate foundation models; create metrics, data, and training frameworks; mentor ML developers; and collaborate across perception, simulation, system design, and safety teams to solve autonomous driving challenges in complex urban environments.

The Senior Analyst in Predictive Analytics and Machine Learning develops and maintains statistical models to support business decisions, monitors model performance, and collaborates with stakeholders across the company.

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 the end-to-end ML execution: build and operate data pipelines, training and evaluation systems, and scalable inference. Fine-tune and optimize large models (LoRA/QLoRA/SFT/DPO/distillation), ensure GPU efficiency, monitor production quality, collaborate with product and application engineering, and rapidly ship reliable, safe ML solutions that translate research into production.

Design, train and validate supervised ML models on multi-billion claim records to score fraud risk; engineer features in-warehouse; deploy and monitor models in production; provide human-readable rationale and claim-level evidence; collaborate with subject-matter experts and stakeholders to ensure actionable, explainable outputs.

Design and deploy production recommendation and retrieval engines, build data orchestration pipelines for multiple modalities, implement agentic workflow frameworks, and prototype, optimize, and scale models with monitoring, governance, and low-latency SLAs.

Build production decision systems that estimate causal effects and uncertainty, learn from interventions, optimize constrained policies (pricing, inventory, promotions), evaluate counterfactuals and challengers, and deploy safe, automated ML-driven business decisions to generate measurable economic lift.

Lead integration of AI/ML (LLMs, RAG, generative workflows) into a cloud-native full‑stack platform. Architect serverless backends, optimize data pipelines, modernize legacy PHP APIs, maintain CI/CD, mentor engineers, and support secure, defense-constrained deployments.

Lead ML engineer responsible for designing and building scalable data and ML systems for LLM development. Own data pipelines, human-in-the-loop labeling, synthetic data generation, automated evaluation, and dataset quality measurement. Collaborate with researchers and engineers, provide technical direction, and mentor teams to link dataset composition to model performance.

Design, build, and deploy scalable AI and machine learning solutions, leading full AI lifecycle, collaborating with teams, and ensuring systems align with client needs.

Design, implement, and optimize ML and deep learning models; preprocess and prepare large datasets; integrate models into production; monitor and maintain model performance; collaborate with engineers and stakeholders; stay current on ML/AI advances.

Design, implement, and optimize machine learning and deep learning models; preprocess and analyze large datasets; integrate models into production; monitor performance and troubleshoot; collaborate with engineers, data scientists, and stakeholders; stay current with ML/AI advances.

Own and build end-to-end matching and ranking systems for out-of-home advertising: design candidate generation, re-ranking, geospatial scoring, data warehouse and feature pipelines, evaluation infrastructure, low-latency ranking APIs, and model monitoring with drift detection and retraining triggers.

Own the ML lifecycle for LLMs applied to manufacturing data: fine-tune and deploy models for document classification, information extraction, table parsing, and multi-page reasoning; build annotation tooling, active learning, and synthetic data pipelines; develop evaluation frameworks; collaborate on AI platform roadmap and optimize model accuracy and production health.

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 and grow a distributed ML engineering team that runs experiments, ships production models, and partners with Risk and platform teams. Balance research bets and delivery, enforce rigorous evaluation and reproducibility, mentor engineers, own roadmap trade-offs, and represent results to stakeholders while improving model performance and production reliability.

The role involves designing, training, and evaluating ML models, optimizing performance, and collaborating with teams to implement AI technologies in products.

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.

The Senior Consultant in AI will design, build, and deliver AI/ML solutions on Azure, engage with clients, and develop project components independently.

Lead the development of machine learning methods for AI-native metal manufacturing systems, focusing on integrating physical data with physics-based simulation.