Top Hybrid Machine Learning Engineer Jobs

Build and productionize computer vision and machine learning systems that convert video into reliable workplace safety incidents. Responsibilities include designing Flink-based perception pipelines, training and optimizing models with Triton and TensorRT, implementing tracking and probabilistic reasoning, improving latency and cost, building observability and evaluation systems, integrating ML tooling, and partnering with platform, customer success, GTM, and data operations teams on deployments and data quality.

Leads AI and machine learning data engineering development projects, including global initiatives. Manages resources and development teams, improves quality and engineering practices, provides subject matter expertise, develops business plans, mitigates risks, handles escalations, and drives technology innovation and change management.

Embed with grocery customers to scope, integrate, and deploy production LLM- and agent-powered systems. Build and harden platform components (knowledge graph/ontology, retrieval, agent frameworks, serving, evals, and observability) so field learnings become reusable tooling. Balance customer-facing implementation with platform engineering to enable scalable, reliable AI in production.

Lead end-to-end post-training of open-weight language models on proprietary legal data, including dataset development, objective design, supervised fine-tuning, preference optimization, reinforcement learning, evaluation, and distributed training. Build production model-serving systems, agents, tooling, and infrastructure. Partner with product engineers, lawyers, and domain experts to translate workflows into model and evaluation strategies and deliver AI capabilities to users.

Develops and evaluates machine learning and deep learning models using biomedical imaging, genomic, transcriptomic, clinical, and text datasets. Builds data pipelines and computational tools, performs feature engineering and validation, conducts cancer genomics research, and contributes reproducible code. Collaborates with computational biologists, clinicians, engineers, and external partners to identify biomarkers and generate scientific insights. Communicates findings through reports, presentations, abstracts, and manuscripts.

Lead neural decoding and machine learning strategy for brain-computer interfaces, taking algorithms from research through real-time deployment in human clinical studies. Develop closed-loop decoders, calibration and adaptation methods, inference runtimes, and clinical deployment processes. Define data and evaluation standards, integrate models across software, firmware, hardware, and robotics, support regulated development, debug cross-functional issues, and mentor ML engineers.

Design and develop machine-learning approaches for vehicle control, including learned vehicle dynamics models, adaptive controllers, data pipelines, evaluation metrics, and simulation tooling. Integrate learned models into safety-critical closed-loop systems and validate algorithms through offline experimentation, simulation, and on-vehicle testing. Collaborate with engineers and research scientists to combine classical control with AI for scalable self-driving technologies.

Own evaluation systems and model-quality improvements for a generative content platform producing AI-generated ecommerce content. Build datasets, rubrics, automated judges, quality gates, and labeled training data from brand feedback. Quantify qualitative improvements, establish approval thresholds, and drive fine-tuning or retrieval experiments into production. The role requires strong software and system design skills, production service ownership, formal machine learning or statistics training, and experience measuring correctness in non-deterministic systems.

Design, optimize, and deploy large language model training and post-training pipelines. Improve model quality through fine-tuning, reinforcement learning, preference optimization, evaluation, and experimentation. Build PyTorch-based infrastructure, optimize distributed multi-GPU training, diagnose performance and convergence issues, and develop production-ready AI systems. Collaborate with researchers, infrastructure engineers, platform teams, and customers while contributing to open-source projects and reusable training capabilities.

Own and improve 2D, 3D, and fusion-based image segmentation models for clinical ultrasound and CT analysis. Build training, evaluation, dataset, and labeling pipelines; collaborate with clinicians, contractors, and data teams; and productionize auditable, low-latency inference services with confidence scoring, drift monitoring, and safe fallbacks. Work under medical-device design controls and support robust modeling across diverse patient anatomy and imaging conditions.