Top Machine Learning Jobs

Advance learning-based 3D reconstruction for photorealistic simulation environments using Gaussian Splatting, NeRFs, and related computer vision techniques. Develop dynamic scene reconstruction, novel view synthesis, multi-sensor rendering, scalable feed-forward models, evaluation benchmarks, and production-quality ML systems. Collaborate with research, infrastructure, autonomy, and product teams while solving customer challenges and influencing architecture.

Build and deploy production machine learning systems, primarily recommendation and personalization models. Own projects across problem definition, data pipelines, model training, deployment, monitoring, and optimization. Develop scalable low-latency inference infrastructure, run experiments and A/B tests, and apply ML to fraud detection, abuse prevention, and content moderation. Collaborate with engineering, product, and data science teams in a fast-paced, in-person startup environment.

Develop and deploy machine learning models and production software for multi-object tracking in autonomous trucks. Responsibilities include sensor fusion across camera, LiDAR, and radar; scalable PyTorch training workflows; tracking algorithm development; large-scale data analysis; C++ and Python engineering; safety and validation strategy; architecture decisions; and cross-functional collaboration. The role also provides technical leadership through design reviews, code reviews, mentoring, and engineering best practices.

Lead research, design, and implementation of machine learning algorithms for computer vision, multimodal data, generative AI, and robotics in surgical platforms. Develop and evaluate vision-action models, datasets, validation methods, and scalable pipelines; integrate algorithms into robotic systems; advance research through patents and publications; and collaborate with engineers, clinicians, users, and academic partners to develop AI technologies for robotic-assisted surgery.

Leads the Scalable Machine Learning team and owns technical strategy for data generation, orchestration, distributed training, and shared ML infrastructure. Responsibilities include managing engineers, setting multi-quarter roadmaps, coordinating GPU scheduling and cloud infrastructure, advancing MLOps practices, influencing peer teams, delivering multi-organization initiatives, and maintaining high reliability across shared systems.

Build and improve AI recruiting agents using modern LLMs, Python backend services, APIs, prompts, RAG, workflows, and agent orchestration. Evaluate model performance, experiment with AI technologies, collaborate with product and engineering teams, and deploy scalable production systems. The role requires Python, LLM and AI API experience, familiarity with agent frameworks and FastAPI, and cloud platform knowledge. Production AI product experience, model evaluation, tool calling, prompt engineering, and open-source contributions are preferred.

Build and own a scalable API platform that enables external organizations to upload data, fine-tune and evaluate AI models, and run low-latency inference for robotics applications. Responsibilities include designing multi-tenant infrastructure, APIs, deployment integrations, observability, reliability, and developer tooling. The role requires strong backend and systems engineering skills, Python expertise, production machine learning familiarity, and end-to-end ownership of ambiguous platform problems.

Develop and optimize CUDA kernels and machine learning software for training and inference on advanced GPUs. Profile workloads, improve GPU utilization, build benchmarking and automation tools, troubleshoot system bottlenecks, support new model architectures, validate numerical and gradient correctness, review code, and contribute to technical documentation and industry events.

Develop and scale distributed machine learning infrastructure for large-scale LLM pre-training. Responsibilities include extending frameworks such as DeepSpeed and FSDP, implementing distributed optimizers, managing multi-node GPU launches, building experiment tracking and monitoring, improving reliability and performance, and maintaining data-loading and checkpointing workflows. The role requires strong Python, systems engineering, distributed training, multi-node debugging, and large-scale pre-training experience.

Build and scale end-to-end reinforcement learning infrastructure for foundation models. Responsibilities include extending distributed training frameworks, integrating rollout and inference systems, implementing reward and trajectory processing, configuring multi-node GPU launches, monitoring experiments, optimizing performance, and developing checkpointing and failure-recovery systems. The role requires strong Python and ML systems engineering experience, hands-on LLM RL pipeline ownership, and expertise with distributed training and policy optimization methods.

Own the applied AI layer of a software platform by building production AI services, retrieval systems, structured extraction pipelines, evaluation suites, guardrails, and agentic workflows. Manage model performance, reliability, latency, cost, and monitoring. Translate product goals and user feedback into measurable experiments and shipped improvements while leading complex projects and maintaining dependable live systems.

Develop and scale machine learning pipelines processing telemetry data to discover rare driving interactions. Benchmark findings, distinguish meaningful patterns from noise, and integrate discoveries into testable artifacts that verify autonomous driving system improvements. The role involves large-scale data and ML pipelines, distributed computing, applied machine learning, statistics, and evaluation methodology, with opportunities to work on autonomous driving and robotics problems.

Develop and optimize large-scale machine learning models, open-source LLM infrastructure, and training and inference systems for advertising applications. Build distributed data and model pipelines, adapt DeepSpeed and OpenRLHF, optimize GPU/TPU serving for latency and cost, monitor model performance, and collaborate with applied scientists on research. The role focuses on recommender systems, personalization, targeting, reinforcement learning, and ad intelligence at massive scale.

Own Arcade’s end-to-end machine learning lifecycle, including training pipelines, model development, evaluation, production telemetry, and releases. Build compact, efficient models for tool selection, routing, retrieval, recommendations, and agent memory. Productionize models for customer VPCs and air-gapped environments through quantization, optimization, packaging, and serving. Set the ML strategy and stack, establish evaluation practices, convert agent traces into training data, and help grow the ML team.

Design and implement enterprise-scale AI/ML architectures and solutions, build and maintain data and model pipelines, process structured and unstructured data, deploy and monitor ML applications, mentor teammates, and operate with autonomy while meeting security clearance requirements.

As an ML Engineer - Robotics, you'll design, train, and deploy intelligent models for autonomous systems, focusing on perception, planning, and decision-making pipelines, while collaborating with robotics engineers to evaluate model performance.

As a Founding ML Engineer, you'll develop ML models for lead optimization, automate demand generation processes, build data pipelines, and collaborate with teams to enhance growth through AI solutions.

Apply machine learning within complex programs and contracts, using advanced programming languages and frameworks such as Python, Java, C, R, PyTorch, and TensorFlow. The role requires expert knowledge of multiple programming languages, applied machine learning experience, and advanced academic training in artificial intelligence, data science, computer science, deep learning, or mathematics. An active TS/SCI clearance with polygraph is required.

Develop, evaluate, and deploy machine learning models for real trading environments; apply rigorous statistical techniques to measure performance, control overfitting, and assess model risk and interpretability; collaborate with traders and developers; maintain best practices and production-ready systems.

Develop predictive and control algorithms for semiconductor manufacturing using physics-based modeling and machine learning. Build scalable sensor-data pipelines, ML surrogates, reduced-order models, and production software integrated with manufacturing tools, control systems, and dashboards. Analyze sensor and metrology data, optimize model performance, and collaborate across physics, engineering, software, and data science teams to deploy intelligent automation solutions.

Lead development of an evaluation platform for agentic AI and LLM systems. Design benchmarking, human review, LLM-as-judge, monitoring, annotation, dataset versioning, and data quality workflows. Own technical architecture, production infrastructure, stakeholder reporting, and platform health while partnering with research, product, and platform teams. Mentor engineers and support scalable AI experimentation and deployment.

Develop and optimize real-time bidding strategies, online auction mechanics, and machine learning models for advertising campaigns. Responsibilities include event prediction, feature engineering, user embeddings, clustering, fraud detection, cross-device identification, data mining, and production support. The role requires advanced Python, strong algorithms and statistics knowledge, machine learning expertise, and substantial RTB or comparable online technology experience.

Designs, develops, tests, deploys, maintains, and enhances machine learning and AI solutions for healthcare. Responsibilities include managing ML codebases and pipelines, collaborating with clinical and research teams, implementing scalable workflows on HPC resources, reviewing code, troubleshooting models and data analysis, supporting publications, and developing reliable production services. The role also involves technical leadership, system debugging, on-call support, and cross-functional collaboration within the Innovation Office.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

Develop, optimize, and deploy production machine learning models for autonomous vehicle perception, prediction, planning, and scene understanding. Own the full ML lifecycle, build scalable training and data pipelines, optimize neural networks with quantization and TensorRT, and integrate models into C++ vehicle systems. Analyze simulation and real-world driving data, improve reliability and latency, and collaborate with autonomy, hardware, systems, and infrastructure teams.