Top Machine Learning Engineer Jobs

As a ML Engineer focused on NLP at Avoma, you will improve the Insights Engine, develop new models with LLMs, maintain code quality, and stay updated on NLP advancements.

As a Staff ML Engineer, you'll lead complex model quality improvements in clinical AI, spanning research and engineering, optimizing performance under high compliance conditions.

Integrate LLMs, RAG, and agentic-AI into secure decision-support software. Design prompts and inference workflows, connect AI to backend services, evaluate outputs, build tests, document designs, and collaborate with engineering, security, and DevSecOps teams to deliver reliable mission-capable AI features.

Lead a team of ML engineers to design, build, and productionize LLM/GenAI and agentic systems (retrieval, tool use, orchestration). Collaborate cross-functionally with MLOps, product, and data science, review code, mentor engineers, run technical evaluations and proofs-of-concept, and ensure reliable, safe ML deployments for a large SaaS product.

Lead assessment, redesign, and hands-on implementation of data pipelines, ML workflows, and platform infrastructure. Produce prioritized system designs, harden production pipelines, improve observability, reliability, and data quality, and mentor the data team.

Build and operate scalable ML inference infrastructure powering Claude’s safety systems. Responsibilities include productionizing safety research, optimizing real-time inference latency and throughput, developing monitoring and observability tools, implementing testing and deployment automation, and supporting reliable classifier rollouts. The role requires collaboration with researchers, inference engineers, security teams, and alignment teams to deliver high-throughput, low-latency, safety-critical ML systems.

Design, build, and operate high-throughput backend services on Claude’s token-generation path. Own latency, reliability, SLOs, incident response, safe deployments, graceful degradation, and cost efficiency. Lead technical direction, design reviews, tradeoffs with inference and research teams, mentoring, and operational improvements across the Safeguards organization.

Design and maintain large-scale data pipelines to generate synthetic text, manage distributed multi-GPU LLM training infrastructure, profile and optimize training and inference code, and deploy efficient inference pipelines for serving LLMs in production. Collaborate across research and engineering and contribute to publishing research and innovations.

Design, prototype, and implement deep learning models for computer vision and SAR geospatial exploitation. Pre-process SAR data, create physics simulations, develop objective functions, assess performance, and deliver production-quality Python code for defense customers.

Design, prototype, and implement deep learning models for computer vision and SAR geospatial exploitation. Develop signal preprocessing, physics simulations, objective functions, and production-quality Python code. Present findings and support transition to operational systems for government customers.

Build and operate scalable infrastructure for Zipline’s machine learning lifecycle, including data ingestion, dataset management, training, evaluation, monitoring, and deployment. Improve reproducibility, observability, orchestration, and performance across autonomy-focused ML workflows. The role requires production software engineering in Python, cloud and on-premise infrastructure experience, Kubernetes, ML frameworks, and strong ownership of secure, mission-critical systems.

Lead design, development, integration, and deployment of end-to-end AI/ML solutions for clinical settings. Collaborate with multidisciplinary teams, build CI/CD and MLOps pipelines, ensure regulatory-quality engineering, mentor junior engineers, define evaluation metrics, and translate technical results into actionable guidance for clinicians and stakeholders.

Design and scale real-time bidding systems, auction mechanisms, bid forecasting, pacing algorithms, and marketplace intelligence models. Apply machine learning, reinforcement learning, control theory, experimentation, and large-scale data analysis to optimize advertiser returns. Lead projects from architecture through offline evaluation and online A/B testing, collaborating with product, infrastructure, and engineering teams to deliver measurable business impact.

Build, deploy, improve, and maintain production machine learning models and systems. Design offline and online experiments, analyze results, and identify actionable insights. Collaborate with ML Infrastructure and cross-functional stakeholders to develop technical platforms and deliver strategic ML product initiatives. Mentor engineers and interns, and help recruit diverse machine learning talent.

Contribute to machine learning model design, training, and evaluation for fraud detection. Communicate findings, develop metrics, and support production ML workflows while ensuring compliance and model governance.

Develop and productionize online mapping machine learning models and algorithms for autonomous vehicles. Analyze system performance, improve data, architectures, and evaluation, build training pipelines, and integrate models with centralized autonomy systems. Collaborate with autonomy, infrastructure, and systems teams to deliver robust mapping solutions using camera and LiDAR data in uncertain environments.

Lead the design and delivery of complex ML systems, architect integrations, and set high standards for engineering practices while collaborating with teams to develop robust solutions and mentor other engineers.

The Staff Machine Learning Engineer will design and deliver complex ML systems, lead integration efforts, build scalable solutions, and optimize model deployments, collaborating with various teams and mentoring peers.

Work on-site with a DHS client to design, develop, train, fine-tune, and deploy AI/ML and generative AI solutions (including LLMs and NLP). Build user-facing AI applications (chatbots, virtual assistants), optimize model performance and scalability, implement MLOps/DevSecOps pipelines, conduct research (prompt engineering, RAGs, fine-tuning), ensure data privacy/security, and brief stakeholders on technical tradeoffs.

Build and deploy real-time multimodal perception systems (camera, LiDAR, radar) for 3D/BEV scene understanding. Optimize models for low-latency embedded inference, design auto-labeling and data pipelines, evaluate metrics, and validate systems on real machines in rugged environments.

Design, develop, and deploy enterprise AI applications using LLMs, RAG, AI agents, vector search, document processing, and cloud-native technologies. Build scalable APIs, integrations, knowledge platforms, and Kubernetes-based services while contributing to architecture, CI/CD, security, testing, and observability. Collaborate with business and technical stakeholders to solve complex problems, evaluate emerging AI technologies, and mentor team members.

As a Machine Learning Systems Engineer, you will optimize distributed training systems, enhance performance profiling, and develop high-performance GPUs for machine learning workloads.

Design and lead large-scale machine learning and backend systems for advertiser growth, including budget pacing algorithms, GenAI creative generation infrastructure, marketplace experimentation, high-throughput billing pipelines, and real-time stream processing. Analyze marketplace and financial data, optimize advertiser performance, ensure billing accuracy and fault tolerance, lead multi-quarter technical roadmaps, and mentor senior engineers.

Lead design, development, and optimization of large language models for SambaNova hardware (RDU). Drive hardware-software co-design, define ML strategy, improve training and inference efficiency, mentor senior engineers, and translate advanced ML techniques into scalable, customer-facing solutions across the product lifecycle.

Develop and prototype computer vision and machine learning solutions for robotic trailer unloading. Train and adapt models for perception tasks such as object detection, segmentation, grasp estimation, depth estimation, and anomaly detection. Troubleshoot real robotic systems, evaluate emerging robotics advancements, transition prototypes to product teams, communicate technical priorities, mentor junior engineers, and provide technical leadership on key projects.