Top Machine Learning Jobs

Develop and deploy machine learning perception and controls systems for a cardiac surgical robot. Build image-to-robot registration, pose estimation, sensor-fusion, data, simulation, and evaluation pipelines using fluoroscopy and echocardiography. Collaborate with robotics, controls, clinical, and physician teams to improve device models and controllers. Deploy real-time models on robotic systems and produce validation and design documentation for FDA design controls.

Develop and evaluate multimodal deep learning and reasoning models for Edge AI, covering data collection, annotation, preprocessing, training, post-training, reinforcement learning, knowledge distillation, and rigorous evaluation. Build agentic and multi-agent reasoning systems, modify transformer architectures, and prototype in Python using PyTorch or TensorFlow. Document findings through reports, publications, and conference submissions while exploring emerging approaches such as world models, JEPA, diffusion, and deep research automation.

The Machine Learning Engineer will design and implement ML pipelines and productionize models, collaborating with scientists and engineers on scalable systems.

Develop and productionize time-series machine learning models for energy market forecasting and battery optimization. Responsibilities include designing evaluation frameworks, improving model performance, analyzing market and experiment data, documenting findings, collaborating with software engineers on APIs and deployment, and supporting energy market strategy. This is a full-time, in-office role in Brooklyn, New York.

Build and operate production machine learning models and risk decisioning systems that detect fraud and abuse across Link. Responsibilities include analyzing large datasets, developing features and experiments, deploying and monitoring models, designing data pipelines, and improving real-time payment authorization. The role requires ownership from problem definition through launch and iteration, with collaboration across Engineering, Product, Data Science, and Risk teams.

Develops and evaluates machine learning and deep learning models using biomedical imaging, genomic, transcriptomic, clinical, and text datasets. Builds data pipelines and computational tools, performs feature engineering and validation, conducts cancer genomics research, and contributes reproducible code. Collaborates with computational biologists, clinicians, engineers, and external partners to identify biomarkers and generate scientific insights. Communicates findings through reports, presentations, abstracts, and manuscripts.

Lead neural decoding and machine learning strategy for brain-computer interfaces, taking algorithms from research through real-time deployment in human clinical studies. Develop closed-loop decoders, calibration and adaptation methods, inference runtimes, and clinical deployment processes. Define data and evaluation standards, integrate models across software, firmware, hardware, and robotics, support regulated development, debug cross-functional issues, and mentor ML engineers.

Lead the technical ML strategy and own scalable machine learning pipelines across Salesforce security and compliance. Develop automated retraining, model optimization, MLOps, infrastructure-as-code, CI/CD, monitoring, and drift detection solutions. Productize generative AI and agentic workflows, collaborate across data and product teams, ensure governance and regulatory compliance, deliver measurable business outcomes, and mentor junior engineers.

Design, productionize, and operate machine learning models and rule-based decision systems for credit underwriting. Build scalable pipelines for feature engineering, training, validation, deployment, monitoring, and continuous improvement. Optimize model performance, collaborate with software, credit, product, risk, and data teams, and promote strong engineering and MLOps practices across production ML systems.

Lead the end-to-end development and operation of machine learning systems supporting customer retention, ranking, matching, recommendations, pricing, and lifetime value growth. Build scalable data pipelines and ML infrastructure across batch and real-time environments, while implementing model monitoring, observability, deployment, and performance optimization. Collaborate across engineering and science teams and promote strong software engineering practices.

Design, train, evaluate, and productionize machine learning models (including embeddings and foundation models) on large-scale data. Partner with product and engineering to run experiments, improve customer experiences, and deploy scalable solutions using Spark and AWS.

Own end-to-end matching and ranking systems for out-of-home advertising: design candidate generation and re-ranking models, build and manage data warehouse and pipelines, implement evaluation and monitoring, publish low-latency ranking APIs, and maintain model observability and retraining workflows.

Build and own production machine learning infrastructure for growth initiatives, including feature pipelines, point-in-time-correct training data, batch inference, model registries, retraining automation, validation safeguards, and production monitoring. Partner closely with data science and data engineering to productionize models, prevent data leakage and train-serve skew, and ensure reliable model-driven promotions.

Leads the design and deployment of production-grade AI and machine learning systems for enterprise workflows and analytics. Integrates LLMs and generative AI, builds end-to-end ML pipelines, establishes AI governance and responsible AI practices, and partners with data engineering, governance, and business stakeholders. Serves as a senior technical authority, makes architectural decisions, sets development standards, mentors peers, and translates complex AI concepts for executive and nontechnical audiences.

Build and maintain large-scale machine learning infrastructure, including training-data pipelines, distributed model-training workflows, and orchestrated ML pipelines. Improve dataset validation, monitoring, testing, reproducibility, and system performance while partnering with ML engineers and researchers to translate research ideas into reliable production systems.

Designs, develops, tests, deploys, and optimizes large-scale machine learning systems and algorithms. The role translates customer and business requirements into scalable, robust data-driven solutions, processes massive datasets, and delivers production insights. Responsibilities include researching emerging machine learning methods, collaborating cross-functionally, developing testing strategies, and integrating new techniques into system architecture.

Develop physics-informed machine learning models for electronic systems. Build automated electrical, thermal, and electromagnetic simulation workflows, generate datasets, create surrogate models, and validate predictions against solvers and experiments. Develop active learning and optimization workflows while owning reproducible pipelines for data generation, training, evaluation, and deployment. Collaborate with hardware and machine learning teams to accelerate electronics development.

Design, train, and validate machine learning models that improve simulated LiDAR, radar, and camera realism for autonomous vehicle simulation. Build datasets from real and simulated sensor data, integrate 3DGS and NeRF reconstruction, develop generative augmentation methods, define sensor realism metrics, reduce sim-to-real gaps, and mentor engineers and interns.

Develop and validate machine learning models that improve simulated LiDAR, radar, and camera realism for autonomous vehicle simulation. Build datasets from real and simulated sensor data, integrate 3DGS and NeRF reconstruction, apply generative augmentation, define sensor realism metrics, close sim-to-real gaps, and mentor engineers and interns.

Build and deploy NLP and LLM-based machine learning models for an AI-powered risk intelligence platform. Responsibilities include text classification, entity extraction, clustering, topic detection, RAG, agentic workflows, semantic search, model evaluation, production monitoring, retraining, and scalable ML service deployment. Collaborate remotely with data engineers, backend engineers, and product stakeholders on high-volume, multilingual text data.

Own and develop Arc Institute’s machine learning infrastructure for virtual cell models, including model evaluation dashboards, single-cell data pipelines, training-set quality controls, and lab-in-the-loop systems. Build scalable train-and-evaluate CI, support distributed multi-node training, improve GPU utilization and throughput, and create validation frameworks for AI-generated software. Partner directly with ML researchers and scientists while setting technical direction for production platforms.

Lead the technical strategy, architecture, development, and productionization of large-scale machine learning and pricing systems for Uber’s real-time marketplace. Develop deep learning, causal, reinforcement learning, optimization, and algorithmic pricing solutions serving billions of trips. Mentor engineers, influence cross-team technical direction, communicate with executives, and deliver high-impact systems for millions of drivers.

Develop and integrate AI/ML capabilities for satellite mission planning, space domain awareness, and resilient operations. Responsibilities include end-to-end machine learning, sensor-data classification and tracking, anomaly detection, scalable HPC pipelines, modeling and simulation integration, algorithm optimization, and technical assessments. The role requires collaboration with satellite designers, operations planners, and subject-matter experts in a fully onsite classified environment.

Develop and deploy machine learning models for autonomous vehicle behavior planning, including onboard planning, offboard evaluation, simulation, imitation learning, and reinforcement learning. Train large models on massive datasets using distributed computing, advance autonomous driving research, and deploy production-ready systems on public-road vehicles. The role also involves developing foundation models, world models, critic models, and neural simulators while maintaining high standards for safety and performance.

Lead design and implementation of post-training alignment pipelines (SFT, RLHF, RLAIF) using proprietary medical data. Improve model calibration, safety, and clinical correctness via novel techniques, distributed training on GPU clusters, and rigorous evaluation frameworks. Collaborate with co-founders to define research roadmap and ship production-ready ML systems.