Top Hybrid Machine Learning Engineer Jobs

Design and build Snowflake’s Cortex LLM post-training platform across public APIs, control plane, and GPU data plane. Scale distributed, fault-tolerant systems for multi-tenant GPU scheduling, routing, training, inference, and reinforcement learning workloads. Optimize performance, throughput, reliability, and cost efficiency while partnering with research teams to productionize advanced LLM training and inference techniques.

Designs, builds, and operates production machine learning systems at scale. Develops deep learning models for sequential, multimodal, and spatio-temporal data supporting risk assessment, engagement, and event detection. Partners with research, product, and platform teams to deploy reliable ML products across cloud and edge environments. Responsibilities include software engineering, AWS-based deployment, complex SQL, large-scale data processing, model orchestration, and cross-functional delivery of measurable mobility and road-safety impact.

Lead the architecture, development, and deployment of scalable machine learning systems, including the AI/ML integration connecting Xometry with partner manufacturing platforms. Build real-time ML serving, data pipelines, MLOps, governance, monitoring, and observability. Drive technical roadmaps, optimize latency and throughput, ensure software quality and security, collaborate across engineering and product teams, and mentor engineers while delivering production AI capabilities.

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.

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.

Supports data science, cloud engineering, and computer vision initiatives for retail IoT products. Responsibilities include analyzing datasets, building dashboards and predictive models, preparing data, supporting Azure infrastructure and automation, monitoring cloud environments, contributing to CI/CD, and assisting with image processing, model training, testing, and validation. Collaborates with software, hardware, engineering, and product teams to develop AI-driven solutions.

Explore, prototype, and productionize AI solutions for clinical data review, focusing on LLMs, agentic AI, RAG, and intelligent workflows. Build modular agent systems, APIs, and interfaces; integrate clinical data sources; evaluate and monitor AI performance; and ensure scalable, reproducible, traceable, auditable solutions aligned with regulatory expectations. Collaborate with clinical experts, product managers, and engineering teams while contributing to AI innovation strategy.

Lead the design, training, evaluation, and deployment of large-scale machine learning models, including LLMs, diffusion models, and GNNs. Own distributed training pipelines, dataset interfaces, optimization strategies, model evaluation, serving, and lifecycle management. Collaborate with data, infrastructure, and scientific teams while providing technical leadership through architecture reviews, mentorship, and cross-functional decision-making.

Designs and implements machine learning models and data pipelines using Python. Acquires and analyzes data from multiple sources, ensures data quality, tunes algorithms, runs large-scale training/inference on cloud, detects data biases, and presents results to technical and business stakeholders. Provides guidance to teams and leads small projects while advancing data science capabilities.

Design and build production LLM infrastructure, including orchestration, prompting, workflow layers, retrieval integrations, MCP tools, evaluation systems, dashboards, and observability. Improve platform reliability through caching, incident response, scalability, and model rollbacks. Establish architecture standards, conduct design reviews, mentor engineers, and communicate AI concepts to nontechnical stakeholders.