Top Hybrid Machine Learning Engineer Jobs

Architect and deploy enterprise LLMs, autonomous multi-agent systems, and real-time AI solutions for supply chain operations. Build secure cloud infrastructure, event-driven pipelines, evaluation frameworks, data integrations, and proactive automation. Lead architectural decisions, establish LLMOps and MLOps standards, mentor engineers, and collaborate with business leaders to deliver production AI products.

Own the end-to-end machine learning lifecycle, including data preparation, dbt pipelines, feature engineering, model development, deployment, monitoring, and evaluation. Apply ML to risk assessment, cost prediction, image analysis, and business operations. Contribute to architecture and strategy while partnering with Engineering, Product, Operations, and Business teams. Build scalable solutions using Python, AWS, Kafka, Django or FastAPI, PostgreSQL, and GitHub Actions.

Lead Osmo’s Applied AI organization building olfactory intelligence models for prediction, formulation, reformulation, molecule generation, and deformulation. Hire and develop ML engineers and research scientists, set the model roadmap and evaluation standards, own enterprise accuracy commitments, design data and annotation strategies, and move research models into reliable production APIs. This hands-on leadership role partners closely with chemistry, sensory science, software, product engineering, and perfumers while remaining involved in code reviews and experimentation.

Founding full‑stack engineer responsible for 0-to-1 product definition, building frontend and backend systems, wrangling agentic AI and ML infrastructure, and leading end-to-end implementation to deliver rapid, high-quality iterations to customers.

Develop and deploy machine learning models for autonomous truck behavior systems using imitation learning, reinforcement learning, and sequence modeling. Build production ML code, training pipelines, data workflows, evaluation tooling, and inference systems. Analyze model performance and failure modes, collaborate with autonomy and simulation teams, and integrate learned behavior models into simulation and validation workflows.

Develop AI-driven software systems for VLSI and circuit design, including machine learning models, combinatorial optimization algorithms, and agentic AI workflows. Analyze datasets, validate hypotheses, and build algorithms for circuit and layout optimization, pre- and post-silicon design, and SPICE correlation. Collaborate across engineering and research teams while advancing electronic design automation solutions.

Designs and integrates AI/ML capabilities for defense, intelligence, and mission systems. The role provides systems engineering expertise, develops operational solutions, supports government acquisition and RDT&E activities, evaluates model performance, resolves system issues, and guides cross-functional teams. It also leads analytical studies, supports tactical-edge and distributed deployments, contributes to technical strategy, and communicates recommendations to government stakeholders and leadership.

Leads the design, development, deployment, and operation of scalable machine learning solutions and high-performance microservices. Builds real-time inference systems using Triton and TensorRT, guides distributed cloud architectures, participates hands-on in coding and reviews, and mentors engineers. Drives technical strategy, operational excellence, system resilience, and business-wide innovation across generative AI, graph machine learning, and big data initiatives.

Develop and productionize machine learning models for industrial process optimization using spectral and multimodal sensor data. Responsibilities include designing experiments, building preprocessing and feature extraction pipelines, monitoring model and sensor drift, detecting anomalies, developing ML infrastructure, and applying chemometrics, hybrid modeling, self-supervised learning, probabilistic reasoning, and time-series techniques.

Take end-to-end ownership of ML problems from framing and experimentation through deployment and support in customer environments. Build, deploy, and maintain production ML models that inform engineering decisions, and communicate technical tradeoffs to cross-functional and non-technical stakeholders.