Top Machine Learning Engineer Jobs

Founding ML engineering hire to design and build production ML systems: training and inference pipelines, serving, feature workflows, monitoring, retraining, governance, and operational practices. Partner across engineering, data, product, and applied science to productionize models, implement observability, prevent degradation, and set platform standards and hiring bar for future ML engineers.

The role involves designing, implementing, and optimizing CUDA-based kernels for autonomous vehicles, ensuring high-performance ML inference for AV software.

Design, build, and operate scalable, low-latency search and retrieval systems powering Atlassian products. Develop and serve production ML models (neural rankers, embeddings, rerankers) using PyTorch and Triton. Implement vector indexing and RAG/agentic retrieval pipelines, ensure observability and cost optimization, manage on-call/runbooks, and collaborate cross-functionally while mentoring junior engineers.

Turn AI/ML research into production: harden models, build scalable backend services and polished UIs, deploy across cloud/on‑prem/edge (including air‑gapped), build CI pipelines, collaborate with customers, and lead engineering best practices across multidisciplinary teams.

Design, deploy, and maintain end-to-end ML risk solutions at scale to detect and prevent fraud, merchant risk, and credit loss. Partner with cross-functional teams, lead technical decisions, build ML tooling, monitor models in production, and investigate emerging abuse patterns to improve detection and decisioning.

Lead design and deployment of scalable ML platforms and tools, manage complex technical projects, mentor junior engineers, and collaborate across teams to enhance autonomous driving systems.

The role demands a Senior Software Engineer skilled in backend development and ML, focusing on delivering ML solutions that address business challenges across various domains.

Design and deliver AI/ML solutions for Business Banking: build NLP/LLM models, embed AI checks into campaign workflows, integrate data pipelines, automate quality metrics, and partner with cross-functional teams to operationalize and measure AI capabilities for marketing and campaign validation.

Design, build, and optimize scalable ML infrastructure for training, evaluation, and high-performance inference. Develop feature generation/serving pipelines, data management and labeling systems, and collaborate with ML engineers to deploy production models while ensuring reliability, scalability, and production-quality code.

Lead ML engineer responsible for productionizing ML systems: design, build, deploy, monitor, and scale models and data pipelines on cloud (AWS). Collaborate cross-functionally, apply MLOps best practices (CI/CD, testing, governance), and maintain high availability and explainable AI standards.

Design, build, deploy, and maintain production ML systems at scale. Collaborate with product and data science teams to create ML architectures, data pipelines, model training and monitoring, CI/CD for models, and ensure performance, reliability, governance, and Responsible/Explainable AI practices.

Develop, deploy, and operate production-grade Nextflow pipelines and cloud-based omics data platforms. Integrate and harmonize complex biological and clinical datasets (single-cell, bulk transcriptomics, ATAC-seq, proteomics, genetics) for ML model training and inference. Implement DevOps and CI/CD practices, manage pipeline lifecycle, collaborate with scientists and external partners, and deliver reusable, scalable data products to support AI-driven discovery.

Lead design and implementation of LLM observability features: prototype and scale product capabilities for tracing, evaluating, and debugging generative AI systems. Work cross-functionally to influence architecture, mentor engineers, prioritize customer pain points, and drive product and engineering decisions for reliable, high-performance AI observability.

Lead design, build, and productionize large-scale ML systems (including RL-based recommenders). Collaborate with Product and Data Science to create data pipelines, cloud architectures, CI/CD, model retraining, monitoring, and Responsible/Explainable AI practices. Mentor and lead teams to deliver performant, resilient ML applications across web, mobile, and email.

Drive technical strategy for the Personalization Platform, develop recommendation systems, maintain ML workflows, and innovate AI solutions for customer engagement.

Lead design, architecture, and deployment of end-to-end ML solutions for onboard autonomous driving. Apply foundation-model alignment, unsupervised pre-training, imitation and reinforcement learning, and scale large-model training pipelines. Collaborate cross-functionally, mentor engineers, conduct research, and productionize foundation models for safe, reliable real-world driving.

Senior individual contributor designing and deploying onboard ML solutions for autonomous driving. Develop models (detection, tracking, trajectory, safe AI), build training pipelines and deployment tooling, translate research into production, collaborate cross-functionally, and mentor engineers.

Design, implement, and deploy ML models for autonomous driving (detection, tracking, trajectory generation). Apply advanced techniques (imitation learning, RL, foundation models), collaborate cross-functionally, and support production ML systems and model deployment.

Build and scale Gusto's ML/AI platform: design MLOps solutions, automated pipelines, model development frameworks, deployment, monitoring, CI/CD, and observability; collaborate with ML engineers and apply AI tools and best practices to production ML systems.

Lead design, build, and operate scalable ML training platform on cloud. Optimize GPU single-node and distributed training, manage Kubernetes-based infrastructure, enable GenAI/LLM fine-tuning workflows, implement observability, improve developer CI/CD and automation, and enforce governance, security, and responsible AI practices.

Lead design and productionalize ML algorithms and pipelines to detect account-sharing fraud in real time. Build scalable preprocessing, training, deployment, monitoring, and A/B testing solutions with cross-functional teams to optimize detection while minimizing user disruption.

Lead design, build, deploy, and maintain production-grade ML/data platforms and end-to-end model pipelines. Engineer large-scale data ingestion, transformation, storage (data lakes/warehouses), monitoring, CI/CD, and production reliability. Resolve production incidents quickly, add features, and drive adoption of enterprise AI-assisted engineering practices while ensuring responsible AI, security, and validation standards across the team.

Lead and mentor multiple engineering teams to deliver AI-enabled MSK platform solutions. Drive AI strategy, design and deploy responsible ML solutions, ensure regulatory (CMS) compliance, oversee vendor partners, modernize platform architecture, and manage production support and incident governance.

Lead design, deployment, and operation of production ML systems and inference pipelines. Build and scale APIs and services on Kubernetes, optimize pipelines, monitor model performance and drift, and partner with cross-functional teams to deliver ML insights to customers.

Design and implement AI/ML systems for healthcare, own inference and delivery, manage/scale production ML systems, build scalable data pipelines and APIs, apply MLOps practices (deployment, monitoring, drift detection), and work with large-scale data and distributed computing frameworks.