Top Senior Machine Learning Jobs

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, 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.

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.

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.

The Senior Machine Learning Engineer will design, implement, and scale advanced foundation models, collaborate with researchers, and build AI systems. Requires hands-on experience with distributed training pipelines and proficiency in AI frameworks.

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.

Design, build, deploy, and operate production-grade AI/ML and LLM systems (including RAG and agentic pipelines). Integrate AI capabilities with product workflows, run evaluation and monitoring, optimize model and system performance, and apply strong engineering practices (testing, CI/CD, versioning).

Lead development of machine learning, computer vision, sensor fusion, and registration algorithms for a robotic lung biopsy platform. Prototype, evaluate, and productionize image-analysis solutions in Python/C++, collaborate cross-functionally, perform failure-mode assessment, and maintain state-of-the-art technical knowledge.

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

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

Design, develop, and deploy machine learning and deep learning models; build end-to-end ML pipelines including preprocessing, training, evaluation, and deployment; perform feature engineering on large datasets; deploy and monitor models on cloud platforms; collaborate with cross-functional teams to integrate AI solutions and retrain models as needed.

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.