Top Hybrid Machine Learning Engineer Jobs

Design, validate, and deploy prognostic algorithms for low-voltage vehicle systems to detect parasitic drains and predict failures. Model system behavior, analyze field telemetry, pilot and validate features in test fleets, and support production rollout. Collaborate with diagnostics, safety, service, and analytics teams to ensure actionable outputs, meet ASIL and regulatory goals, and develop long-term prognostics strategy across programs.

Leads the architecture and production delivery of a novel AI-powered exploitability analysis engine. Owns probabilistic ranking, graph modeling, entity resolution, calibration, validation, and security guardrails for agentic systems. Drives zero-to-one development, technical direction, architecture and code reviews, cross-functional partnerships, and mentorship. Requires deep expertise in production AI/ML, LLMs, distributed systems, cloud-native development, probabilistic modeling, and scalable architectures, with strong security experience preferred.

Leads the technical vision and architecture for AI-powered exploitability analysis in cybersecurity. Defines advanced modeling approaches, enterprise-scale engineering standards, AI safety and governance practices, and portfolio-wide architecture. Guides multiple teams without direct authority, mentors senior engineers, represents technology to executives and customers, and delivers production AI/ML products using LLMs, agents, retrieval, probabilistic scoring, and graph technologies.

Own the architecture and production delivery of a novel AI-powered exploitability engine. Lead probabilistic ranking, graph modeling, entity resolution, calibration, validation, and security guardrails for agentic systems. Drive zero-to-one technical direction, architecture, code reviews, cross-functional partnerships, and mentorship. Build scalable systems using distributed technologies, cloud-native development, APIs, databases, and machine learning while establishing defensible engineering decisions and evaluation metrics.

Own end-to-end machine learning projects for marketplace health, trust, and safety. Develop and deploy machine learning and LLM solutions, design experiments, monitor models, evaluate business impact, and improve systems as marketplace behavior evolves. Collaborate with product, engineering, operations, and trust and safety stakeholders while translating ambiguous business challenges into scalable modeling approaches and communicating recommendations clearly.

Own and evolve the GenAI Evaluation Framework as an internal product. Build LLM-as-judge, rule-based, and statistical evaluators; create evaluation pipelines, golden datasets, dashboards, and quality reporting; integrate agents with instrumentation and CI checks; onboard product teams; and maintain documentation, roadmaps, monitoring, and runbooks.

Leads the design, development, deployment, and operation of agentic AI and large language model platform capabilities. Builds reusable services, evaluation and observability systems, safety guardrails, and governance practices for enterprise-scale AI products. Establishes engineering standards through system design, code reviews, mentorship, and documentation while partnering with product, security, data, risk, and stakeholder teams to deliver reliable, measurable outcomes.

Lead marketing measurement and experimentation to quantify attribution and incrementality, optimize ad platforms and CRM, build performance data assets, analyze lifecycle engagement, and communicate insights to influence marketing strategy and spend allocation.

Develop and deploy machine learning systems for humanoid robot manipulation, covering data collection, annotation, synthetic data generation, model training, simulation, hardware deployment, and fine-tuning. Diagnose vision, calibration, sensor fusion, model, and data bottlenecks to improve reliability. Build real-time robotic software using modern computer vision and deep learning, while mentoring engineers and driving architectural excellence across the robotics organization.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Participate in a 12-month quantitative analytics program with two rotations focused on applied AI, machine learning, generative AI, agentic systems, and advanced modeling. Develop, fine-tune, evaluate, optimize, and deploy language models and scalable AI pipelines. Work with LLM agents, RAG, distributed training, model monitoring, and quantitative validation while collaborating with technical and business teams on financial-services challenges.

Develop and test machine learning models, pipelines, agentic systems, and LLM-powered applications under senior-engineer mentorship. Interns will work across the ML lifecycle, including problem framing, model selection, pipeline development, evaluation, and deployment support. The role involves collaborating with ML Engineers, Product Managers, Designers, and Full-Stack Engineers on scalable AI solutions.