Top Machine Learning Engineer Jobs

Designs, builds, and evaluates language models for AI security products. Responsibilities include dataset curation, preprocessing, LLM experimentation, safety and robustness evaluation, dashboards, knowledge graphs, model fine-tuning, production integration, monitoring, CI/CD, and ML tooling. Collaborates with researchers and data scientists to automate quality assurance and guardrails while delivering reliable, scalable, and interpretable models.

Set the technical direction for machine learning at Payabli by maturing production risk models and launching new models across the payments lifecycle. Establish experimentation, monitoring, drift detection, benchmarking, retraining, and incident-response foundations. Translate payments challenges into modeling opportunities, connect model performance to business outcomes, influence the ML roadmap, and mentor engineers while partnering with product, engineering, and risk operations.

Design, build, and deploy production AI/ML and generative-AI solutions (including RAG and agentic workflows) to enhance sales and distribution platforms. Develop scalable components, API integrations, microservices, and automation pipelines; monitor model performance; ensure Responsible AI, security, and enterprise governance; and collaborate with cross-functional and offshore teams to deliver reliable, high-performing systems.

Design, implement, and maintain scalable ML pipelines for training, deployment, and monitoring. Automate data preprocessing, feature engineering, and validation. Productionize models with data scientists and backend engineers, ensuring reliable, secure, and efficient ML infrastructure and optimized training and inference performance.

Technical lead for end-to-end ML and CV pipelines to reconstruct, validate, and maintain map primitives from multi-modal vehicle sensor data. Architect scalable offline and production map reconstruction systems, define technical strategy, drive cross-functional integration, mentor senior engineers, and ensure robustness, safety, and operational scalability for national-scale mapping.

Design, train, and productionize underwriting ML models for real-time transaction decisions. Build feature pipelines and training datasets, run experiments, integrate models into batch/real-time systems, and monitor data and model health while collaborating with cross-functional teams.

Develop generative and predictive machine learning models for therapeutic antibody sequence, structure, and property design. Build multi-objective optimization and agentic LLM methods for lab-in-the-loop protein engineering, deploy models with engineering teams, and support drug development programs. Collaborate with drug developers, wet-lab scientists, automation specialists, and data scientists to translate ML methods into validated antibody candidates.

Design, productionize, and scale machine learning and optimization models, pipelines, libraries, and deployment platforms. Partner with data scientists, engineers, product managers, and business stakeholders to deliver ML capabilities, personalization systems, and reusable end-to-end workflows. Establish ML engineering best practices, troubleshoot data pipelines, mentor junior professionals, and help define BetterHelp’s ML platform strategy.

Lead the strategy, architecture, and production delivery of large-scale machine learning systems for recommendations, matching, ranking, retrieval, and interaction prediction. Own the full model lifecycle from research and feature engineering through distributed training, experimentation, evaluation, and latency optimization. Mentor machine learning engineers and data scientists, establish organizational best practices, and partner with engineering and product leadership on the ML roadmap.

Design, build, and optimize LLM- and RAG-powered applications for government and enterprise missions. Develop document ingestion, embeddings, vector retrieval, LLM orchestration, traditional machine learning models, APIs, and cloud-native containerized workloads. Apply software engineering, machine learning, MLOps, security, and model evaluation practices to deliver reliable production systems. Collaborate with data engineers, architects, and mission subject-matter experts to create scalable solutions.

Build and optimize distributed machine learning infrastructure for large-scale model training and inference. Responsibilities include profiling training runs, improving compute, communication, and storage efficiency, developing parallelism and quantization strategies, creating CUDA and Triton kernels, and ensuring fault tolerance, checkpointing, deterministic orchestration, and reliability across GPU clusters. The role partners closely with research scientists to scale new model architectures and training recipes.

Design and deploy production-grade ML systems, integrate models into software pipelines, and develop features leveraging ML for optimization and prediction problems.

As a Machine Learning Engineer in the Buyer Growth team, you'll design and deploy ML models to drive user acquisition and improve retention. You'll manage the entire model lifecycle and collaborate across teams to identify impactful growth opportunities.

The role involves building machine learning systems for Radar's products, focusing on core ML infrastructure, anomaly detection, geolocation, and collaboration with customers.

Build and scale machine learning systems for frontier AI data generation and evaluation. Responsibilities include efficient agentic evaluations, model routing, fine-tuning and serving smaller open-weight models, predictive task-difficulty modeling, LLM-as-judge measurement, and productionizing research prototypes. The role requires rigorous experimentation, statistical analysis, strong Python and software engineering, and end-to-end ownership of production ML or software systems.

Lead the technical direction, architecture, and delivery of large-scale machine learning systems for autonomous vehicle data. Develop autonomy algorithms, foundation models, deep scene understanding, causal models, high-quality datasets, and auto-labeling systems. Mentor senior engineers, define ML platform requirements, lead complex projects from conception through production, and coordinate integration across Uber engineering teams.

Develop and optimize machine learning models for Waymo’s autonomous driving perception systems. Responsibilities include implementing LoRA and QLoRA fine-tuning in JAX/Flax, building knowledge-distillation pipelines, validating quantization and model optimization techniques, benchmarking latency, and evaluating perception performance across large autonomous-driving datasets. The intern will collaborate with model optimization, hardware, and latency teams while working on multi-task vision transformers and onboard deployment.

Build and maintain Quora’s machine learning platform and distributed model-serving infrastructure. Develop scalable systems for recommendation and language models, optimize GPU serving latency, throughput, and cost, improve ML tooling and feature stores, and contribute to platform modernization. The role includes production deployment, collaboration with senior engineers, mentoring, and participation in an on-call rotation.

Build and maintain MLOps infrastructure: CI/CD pipelines, containerize and deploy ML models, manage training and batch prediction workflows, and ensure scalable, highly available production ML systems while collaborating with researchers and data scientists.

Build and productionize applied AI and machine learning capabilities for Crucible, including LLM workflows, retrieval, tool use, orchestration, evaluation, and model-serving systems. Integrate optimization and forecasting models into reliable products, select models for constrained cloud, air-gapped, and edge environments, and establish technical standards for production AI. Collaborate with manufacturing, supply chain, data science, software engineering, and product teams to deliver measurable operational value.

Build and maintain computer vision and machine learning pipelines for automated device inspection, grading, and quality assessment. Responsibilities include data preparation, feature engineering, model training and evaluation, production Python development, monitoring, annotation workflows, documentation, code reviews, and collaboration with product and operations teams. The role supports the full ML lifecycle and offers increasing ownership under senior engineering guidance.

Develops and advances machine learning and deep learning models for personalized advertising recommendations. Responsibilities include understanding advertising systems and user behavior data, conducting foundational model research, validating approaches with large-scale compute, analyzing metrics, and collaborating with engineering and product teams to productionize research and deliver measurable business results.

Build and calibrate an agent evaluation platform for multi-step LLM agent trajectories. Design rubrics, deterministic validators, LLM judges, confidence scoring, and human calibration workflows. Fine-tune judge models, analyze simulation-versus-production divergence, and develop process reward models for agent optimization. Establish versioned scenarios, simulated environments, and reliable measurement standards while maintaining production-grade Python code.

Design, build, and deploy production-grade AI/ML and LLM solutions for claims processing, eligibility, prior authorization, and payment integrity. Build scalable data pipelines, integrate models with core payer systems, ensure HIPAA/PHI-safe pipelines and Responsible AI governance, monitor and retrain models, and partner with cross-functional teams to deliver reliable, CI/CD-driven solutions.

Designs and builds high-performance data infrastructure for machine learning model training and feature serving. Responsibilities include developing distributed processing architectures, optimizing training data pipelines, resolving performance bottlenecks, collaborating with ML research teams, and establishing tooling and best practices for ML data infrastructure. The role requires experience with fault-tolerant distributed systems and frameworks such as Apache Spark or Flink.