Top Machine Learning Engineer Jobs

Drive AI/ML solution sales for cable, media, and entertainment clients by developing POCs, proposing solutions, advising customers, building roadmaps, coordinating offshore delivery teams, and performing data-driven analysis to identify business improvements and implementation plans.

Design and build production systems for AI inference, optimize runtime services, and collaborate on AI solutions with researchers and engineers.

The Manager of Data Science will lead a team focused on machine learning model development, team management, and cross-functional collaboration to advance product goals.

Lead requirements and systems engineering for an enterprise application integrating AI capabilities. Work with customers, developers, systems engineers, and domain experts to define requirements, create technical specifications and Jira stories, validate features, evaluate AI architectures and model performance, and coordinate AI solution delivery through Agile development. The role requires hands-on technical execution, functional testing, stakeholder communication, and a current Top Secret/SCI clearance with favorable polygraph.

Responsible for fine-tuning state-of-the-art LLMs, optimizing models for deployment, and driving AI architectural decisions. Collaborate with cross-functional teams to develop impactful AI products.

Build and deploy video recommendation models and APIs, research LLM and computer vision applications, and develop semi-automatic annotation techniques for NLU models. Retrain and evaluate machine learning systems, improve Roku Voice data annotation pipelines, collaborate with engineering and product teams to launch research into products, and document technical work through design documents, demos, and presentations.

Researches, designs, and validates machine learning, sensor-fusion, tracking, estimation, and probabilistic models for real-time airspace awareness. Develops algorithms such as Kalman filters, particle filters, Bayesian filters, and multi-hypothesis tracking; conducts simulations, benchmarks performance, analyzes multimodal datasets, quantifies uncertainty, and partners with software engineers to deploy production-ready systems.

Lead development of complex data pipelines, AI/ML systems, and software solutions. Provide technical leadership, mentor engineers, conduct code reviews, make architectural decisions, and collaborate with technical and non-technical stakeholders. Maintain hands-on Python development using scalable data engineering practices, cloud platforms, containerization, CI/CD, and machine learning frameworks. Experience with large language models and AI agents is highly desirable.

Own and develop a production recommendation engine for Kubernetes resource optimization. Design time-series forecasting models, data-quality pipelines, safety guardrails, regression tests, and production metrics for CPU, memory, GPU, and JVM workloads. Investigate customer issues, improve recommendation accuracy, maintain Python services, and guide ML strategy while collaborating with platform engineering, product, and leadership.

Build and maintain production ML and computer vision systems for automated battery sorting: image acquisition, inference, sensor integration, model training/deployment, observability, CI/CD, and cross-functional collaboration to improve on-floor performance.

Design, develop, and deploy scalable ML software systems for healthcare. Collaborate with teams to improve products and ensure performance and reliability.

As an AI/ML Engineer, you will develop and optimize machine learning models, collaborate with teams, and enhance mobile app features using AI techniques.

As a Research/ML Engineer, you will translate cutting-edge AI research into product solutions, develop safety and reliability systems, and collaborate with technical teams.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

As an ML Engineer, you'll deploy models and create tailored AI solutions for clients, ensuring impactful integrations and customer success.

Design, build, and productionize scalable AI systems for autonomous detection, RAG-backed investigations, and auto-remediation. Develop LLMs, graph-based reasoning engines, streaming pipelines, evaluation/red-team processes, and collaborate with cross-functional teams to translate attacker behavior into robust detections.

Develop and deploy lightweight machine learning models for edge AI, working on hardware integration and optimization techniques while mentoring junior engineers.

Design, develop, and productionize scalable AI systems for autonomous threat detection and response. Build LLM-based reasoning engines, graph-based analytics, streaming feature pipelines, orchestration frameworks, and evaluation/reliability tooling. Collaborate with product, detection engineering, and customer teams to translate attacker behavior into robust ML and rule-based detections.

Lead design and delivery of scalable AI systems for autonomous threat detection and response. Build and productionize LLMs, graph reasoning engines, streaming feature pipelines, and orchestration frameworks. Own evaluation, reliability, and cross-functional collaboration while mentoring engineers and experimenting with RAG, tool-calling agents, and multi-modal models.

The Machine Learning Engineer will develop and optimize models, manage datasets, enhance training pipelines, and collaborate across teams to innovate in autonomous vehicle technology.

Build and enhance ML systems for custom model training, oversee projects from data intake to model delivery, ensure model quality at scale, create evaluation frameworks, and collaborate with engineers.

Seeking a Machine Learning Engineer to build models from idea to delivery, maintain production models, and develop system architecture while collaborating on data-driven strategies.

Design, develop, and deploy machine learning models; analyze large datasets; build scalable data pipelines and distributed computing environments; integrate ML solutions with cross-functional teams; optimize performance and support production ML infrastructure.

Design, implement, and deploy machine learning algorithms and scalable ML solutions; collaborate with data scientists, engineers, and federal customers to build analytics capabilities and features; support application deployment and customer enablement.

The Machine Learning Engineer will develop software solutions, optimize data pipelines, and deploy machine learning models using cloud platforms and best practices.