Top Machine Learning Engineer Jobs

Early-career ML engineer who builds, evaluates, and improves LLM-powered financial applications. Design and implement ML models, test and analyze LLM failure modes, apply statistics and experimental design, collaborate with cross-functional teams, ensure responsible AI practices, document results, and support junior teammates.

Lead design, development, and deployment of machine learning models and cloud-native MLOps pipelines for naval/DoD applications. Work across multidisciplinary teams to implement scalable, secure ML solutions, support CI/CD and DevSecOps, and meet shipboard and operational requirements.

Lead design, development, and deployment of deep learning models for pathology image analysis. Drive experiments, optimize models for production, support data and ML infrastructure, implement semi/self-supervised methods, collaborate with clinical and product teams, and ensure validated, scalable medical device software.

Develop and productionize ML solutions for surgical workflow and robotic platforms. Build MLOps pipelines, curate datasets, train and deploy deep learning models, manage cloud and edge infrastructure, and collaborate across engineering and data science teams to operationalize models and monitoring.

Develop and optimize large-scale simulation and RL infrastructure for training surgical robots. Build multimodal data pipelines, generate synthetic datasets, perform sim-to-real validation, and integrate simulation outputs into production ML pipelines. Deliver production-ready code, optimize GPU/inference/rendering performance, and collaborate with clinical advisors to iterate prototypes and deploy physical AI systems.

Lead development and delivery of multimodal ML solutions for surgical robotics: design experiments, train and validate vision foundation models, build end-to-end ML pipelines, optimize models for production, collaborate with clinical and product teams, and translate algorithms into regulatory-aware, production-ready medical device software.

Develop, deploy, and optimize machine learning models (object detection, signal processing, predictive analytics) and data pipelines. Perform model evaluation, performance testing, and system-level debugging. Collaborate with engineers to integrate AI/ML into mission-critical systems, document designs, and support validation, verification, and safety/compliance activities.

Design, develop, and deploy advanced AI/ML models (deep learning, RL, generative) for aerospace and defense. Architect scalable systems, integrate with hardware, lead projects, validate and verify models, mentor junior engineers, and ensure compliance and safety for mission-critical applications.

Technical lead for rapid AI/ML prototyping in autonomy, perception, and analytics. Architect, build, deploy, and validate end-to-end ML systems (training, inference, deployment), mentor engineers, set technical direction, manage risks, and demonstrate mission-focused prototypes in aerospace and defense environments.

Lead AI/ML technical vision and architecture across multiple programs, driving R&D in autonomy, perception, generative AI, and large-scale ML systems. Define standards, infrastructure, and MLOps practices; oversee end-to-end system design, validation, and deployment; mentor engineers and represent the team to leadership, customers, and partners.

Lead design and management of search and content recommendation services and models. Build personalized recommender systems using ML/AI, define success metrics with product and business teams, drive core module development, and translate PB-scale data insights into measurable product impact.

Design, develop, and operationalize ML and computer vision models for imagery analysis, focusing on detection of AI-generated or manipulated images. Preprocess and validate large datasets, evaluate and optimize model performance, containerize deployments, translate research into production workflows, and collaborate in secure, classified environments.

Design, build, and validate ML models for RF emitter identification; perform exploratory data analysis on sensor NDF datasets; implement CPU-optimized ML pipelines for constrained edge x86 Linux hardware; diagnose data quality issues; collaborate with researchers and engineers; produce reproducible experiments and technical documentation for R&D programs.

Lead end-to-end AI lifecycle for clinical applications, designing and deploying multimodal, foundation-model, and vision/NLP solutions. Guide architecture, evaluation, compliance, CI/CD/MLOps, and risk/validation for regulated healthcare deployments while mentoring engineers and collaborating with clinicians and stakeholders.

Design, develop, and deploy AI/ML solutions (time series, LLMs, vision, NLP) for finance-related healthcare operations. Collaborate with multidisciplinary teams, build CI/CD and MLOps pipelines, ensure quality, regulatory compliance, and usability, and train staff on AI tools.

Lead end-to-end AI/ML lifecycle for clinical applications: design foundation and multimodal models, develop and deploy production-grade solutions, ensure regulatory compliance and quality systems, build CI/CD and MLOps tooling, provide technical leadership, mentorship, and cross-functional consultation to integrate AI into clinical workflows.

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.

Design, deploy, and optimize cloud-native ML systems on AWS, integrate foundation models (Bedrock/Anthropic), build and monitor SageMaker pipelines and data pipelines, detect drift, extend ML infra and libraries, and collaborate cross-functionally to deliver secure, cost-efficient production ML services.

Integrate state-of-the-art machine learning (especially generative) into production-grade sensor simulation for LiDAR, Radar, and Camera. Collaborate with research, rendering, and physics teams to develop, evaluate, and deploy models and tooling that produce production value and customer impact.

Design, develop, fine-tune, and deploy generative AI models (GANs, VAEs, transformers). Collaborate with cross-functional teams, troubleshoot model issues, document work, and communicate technical concepts to non-technical stakeholders while staying current on research.

Design, build, and deploy production-grade ML and AI systems end-to-end, combining classical ML, deep learning, and LLM/agentic integrations. Implement MLOps, monitoring, CI/CD, model serving, vector search/RAG pipelines, and scalable APIs; mentor engineers and drive engineering best practices.

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.

Lead a team of 4-5 ML engineers to design, build, and operate scalable AI/ML systems and agentic architectures. Provide architecture and code guidance, establish ML engineering best practices, standardize LLM serving and retrieval infrastructure, implement safety guardrails and traceability, define evaluation frameworks for reasoning quality, and ensure CI/CD, data governance, and responsible AI compliance.

Lead design, build, and operation of scalable AI/ML systems (LLM infrastructure, RAG pipelines, autonomous agents). Provide architecture and code guidance, establish ML engineering best practices, CI/CD, safety guardrails, evaluation frameworks, and ensure compliance with data governance and responsible AI standards.

Design, build, and deploy scalable ML pipelines for multimodal neuroscience data. Research, train, and deploy advanced deep learning and reinforcement learning models to predict brain states, detect pathological patterns, and generate personalized neuromodulation parameters. Select AI frameworks and cloud infrastructure, collaborate with computational neuroscientists, and contribute to IP and publications.