Top Machine Learning Engineer Jobs

Build and own ML infrastructure for distributed training across GPU clusters, including job launching, checkpointing, recovery, monitoring, profiling, experiment tooling, and reliable data pipelines. Debug failures across GPUs, networking, numerics, and data while partnering closely with research. The role suits an early-career builder with strong Python skills, practical ML tooling experience, public work, high agency, and a willingness to learn quickly in a small, autonomous team.

Lead applied science strategy for AI-powered products, defining success metrics and building rigorous evaluation frameworks for LLM, agentic, retrieval-augmented, and multimodal systems. Develop offline experimentation and monitoring pipelines, guide model and product decisions, partner with engineering and product leaders, establish launch quality standards, and mentor scientists and engineers while ensuring production reliability, scalability, and responsible AI practices.

Develop, evaluate, and integrate machine learning models for physical robots. Build data and evaluation workflows, run research experiments, analyze failures, and communicate tradeoffs. Integrate models into robot software, test them on real hardware, and optimize inference for practical latency and compute constraints. Collaborate with robotics and software engineers to turn research into reliable product capabilities.

Develop and improve Jane Street’s ML compiler stack for high-throughput training and low-latency inference. Responsibilities may span compiler optimization, graph compilation, GPU kernel programming, frontend design, ML training and inference systems, debugging, formal methods, and programming-language or DSL development. The role involves evaluating existing technologies and building custom solutions to improve hardware utilization and accelerate model development and deployment.

Own fraud prevention machine learning end to end, including deploying anomaly detection and device risk models, building labeled datasets and evaluation methods, designing online and offline features, modernizing training and serving infrastructure, investigating fraud patterns, and monitoring production systems. The role partners with data engineering, backend, and product teams and participates in on-call operations.

Lead the architecture and production deployment of predictive ML and GenAI capabilities that improve clinical outcomes and member retention. Develop causal ML strategies, intervention-effectiveness models, reusable GenAI platform services, evaluation frameworks, safety guardrails, and cross-functional AI roadmaps. Partner with Product Engineering, ML Ops, Analytics, and clinical stakeholders while setting technical standards, mentoring engineers, and driving multi-quarter initiatives.

Leads enterprise adoption of AI, machine learning, generative AI, and intelligent automation across federated engineering teams. Defines reference architectures, standards, reusable patterns, and Responsible AI governance while advising delivery teams on secure, resilient, scalable implementations. Influences senior stakeholders, promotes AI-assisted engineering practices, establishes validation standards, and builds communities of practice to improve consistency, delivery speed, and operational readiness.

Designs, trains, deploys, and maintains deep learning models and ML pipelines for electro-optical and infrared image processing. Develops computer vision solutions for space-based defense and intelligence applications, including object detection, segmentation, and tracking. Collaborates with AI and engineering teams, prototypes solutions for operational systems, troubleshoots software, and evaluates emerging ML tools and techniques.

Build and maintain data ingestion, transformation, and ETL/ELT pipelines; support data modeling, warehousing, quality validation, governance, cloud platform deployment, and analytics environments. Collaborate with analysts, data scientists, and product teams, participate in Agile ceremonies and code reviews, and document data flows, lineage, and standards.

Build and deploy production machine learning systems for quoting, material selection, planning, and factory decisions. Responsibilities include developing datasets, features, models, evaluation systems, and pipelines; monitoring quality, drift, latency, and business impact; designing safeguards and fallbacks; improving data quality and explainability; and collaborating with software engineers, operators, and domain experts.

Build and operate production search, ranking, recommendation, and multimodal AI systems for a global jewellery marketplace. Own machine-learning models end to end, including feature development, training, evaluation, deployment, monitoring, and low-latency serving. Develop contextual and conversational search, image-based retrieval, learning-to-rank systems, and embedding pipelines. Define ranking experiments, evaluate LLM behavior, and design graceful recovery from probabilistic failures across multi-tenant storefronts.

Leads AI and machine learning data engineering development projects, including global initiatives. Manages resources and development teams, improves quality and engineering practices, provides subject matter expertise, develops business plans, mitigates risks, handles escalations, and drives technology innovation and change management.

Design, build, deploy, and scale production machine learning systems across data processing, training, evaluation, inference, and operations. Convert research innovations into reliable product capabilities, develop reusable infrastructure, resolve performance and scalability challenges, contribute to architecture, and coordinate complex technical projects. The role also emphasizes security, privacy, AI safety, Responsible AI, cross-functional collaboration, and mentoring engineers.

Lead quality engineering for machine learning products by designing scalable automated test frameworks, validating probabilistic model behavior, and testing APIs, microservices, databases, performance, and reliability. Partner with ML, software, product, data, and operations teams to implement continuous testing, analyze anomalies, maintain test environments, and establish release-quality practices for production-scale inference services.

Own the infrastructure, deployment, monitoring, versioning, and rollback systems that move computer vision and machine learning models from development into reliable cloud and edge production environments. Process and optimize pipelines handling 11 million daily images, support roadside hardware deployments, detect model degradation, and manage updates and retraining. The role requires strong production software engineering skills, adaptability, and interest in operationalizing ML at scale.

Build production AI and machine learning software for large-scale computing and AI infrastructure. Responsibilities include developing anomaly detection, forecasting, root cause analysis, LLM workflows, agentic systems, and operational analytics using Go, Python, and React. The role spans data engineering, model development, evaluation, deployment, monitoring, and continuous improvement while collaborating with engineering, architecture, product, and customer teams.

Develop and promote software safety practices for autonomous-driving products. Partner with deep learning engineering teams to ensure software, hardware architectures, and development processes meet ISO 26262 and automotive safety standards. Guide teams through safety activities across concept, design, development, and validation phases, support audits, and define quality and safety-conformance processes for mission-critical applications.

Develop scalable AI-powered operations and ML infrastructure for reliable, secure cloud systems. Design distributed systems, write maintainable code, improve architecture and operational excellence, mentor engineers, establish engineering standards, and lead testing, incident response, and on-call activities. The role requires Azure or cloud experience, coding proficiency, and expertise in automation, reliability, CI/CD, infrastructure as code, and responsible AI practices.

Own and optimize deep learning training performance across GPUs, cloud infrastructure, and distributed environments. Profile workloads, develop custom GPU kernels, tune PyTorch and training systems, improve memory and compute efficiency, and design scalable distributed training strategies. Collaborate with researchers and infrastructure teams to preserve numerical correctness while improving speed, cost, reliability, and scalability. Build benchmarking, checkpointing, recovery, and regression-testing workflows for long-running AI/ML experiments.

Build and evaluate secure machine learning systems, investigate AI vulnerabilities and adversarial attacks, develop defensive techniques, prototype emerging technologies, and transition AI capabilities to government sponsors. Collaborate with researchers and technical teams, communicate complex concepts, mentor junior staff, and support applications including computer vision, NLP, planning, robotics, and trustworthy AI.

Develop and deploy reliable machine learning, LLM-powered agent, and agentic workflow systems for government and enterprise customers. Integrate models with tools, APIs, MCP servers, and data sources; build evaluation frameworks for performance, reliability, and safety; improve Chariot, the company’s AI operations platform; and support mission-critical deployments. Collaborate with data scientists, software engineers, DevOps teams, and customers in secure government environments.

Develop and deploy reliable machine learning, LLM, agentic, and data-centric systems for government and enterprise customers. Build and evaluate AI agents, workflows, tool integrations, and retrieval-augmented systems; measure performance, reliability, and safety; and improve the Chariot AI operations platform. Collaborate with customers, data scientists, software engineers, and DevOps teams on mission-critical deployments. The role is fully remote or hybrid in Austin, with travel up to 20%.

Develop and productize classical computer vision, machine learning, and deep learning algorithms for semiconductor defect detection, classification, metrology, and image analysis. Responsibilities include literature research, feasibility studies, prototyping, validation, benchmarking, production-quality coding, software integration, and compute optimization. The role collaborates with infrastructure, software, systems, and applications teams to deliver solutions for semiconductor process-control tools.

Develop and deploy machine learning models, datasets, algorithms, and training recipes to improve AI safety for multimodal LLMs and agentic systems. Work across LLM security, frontier risks, agentic safety, multi-turn evaluation, content safety, hallucinations, and fairness. Apply post-training methods including SFT and RLHF/RLAIF, conduct safety evaluations and research, and collaborate cross-functionally with engineering, data science, and research teams.

Develop and evaluate signal processing, image processing, machine learning, and computational algorithms for ultrasound-enabled medical devices. Implement real-time software, analyze bench, preclinical, and clinical data, validate algorithms, assess technical feasibility, and guide design decisions. Collaborate with engineering, clinical, design, and research teams while providing technical leadership and mentorship in algorithm development for cardiac electrophysiology applications.