Top Machine Learning Jobs

Develop and scale vision-language-action models for robotic manipulation, owning dataset construction, training recipes, distributed GPU infrastructure, evaluation, and on-robot validation. Build reproducible ML pipelines, optimize multimodal data processing and model training, diagnose failures across data and robot behavior, and translate research into production-quality systems. Collaborate across research, robotics, hardware, data, and operations teams while mentoring engineers.

Build evaluation infrastructure for Aircall’s AI voice, chat, and messaging agents. Responsibilities include designing benchmarks and metrics, training and fine-tuning TTS, ASR, and speech-to-speech models, developing automated regression and release gates, calibrating LLM-as-judge systems, analyzing failures, monitoring production quality and drift, and evaluating voice-specific scenarios such as accents, noise, barge-in, DTMF, latency, and tool failures.

Lead design and productionalize ML algorithms and pipelines to detect account-sharing fraud in real time. Build scalable preprocessing, training, deployment, monitoring, and A/B testing solutions with cross-functional teams to optimize detection while minimizing user disruption.

Architects, develops, and deploys high-performance machine learning systems across the full lifecycle. Responsibilities include large-scale feature engineering, online and offline pipelines, tree-based and deep learning models, low-latency serving, MLOps automation, model monitoring, and rigorous experimentation. The role requires production-grade software engineering, distributed computing, statistical expertise, and experience shipping ML systems in high-scale environments.

Design, deploy, optimize, and maintain advanced machine learning models for monetization applications. Translate AI research into scalable production systems, build data pipelines, improve model performance, collaborate with research, engineering, product, and design teams, and contribute to model monitoring, code reviews, and end-to-end problem ownership.

Designs and implements AI-enabled workflows for cyber and intelligence missions, including LLM-based agents, multi-step automation, API and data-store integrations, and production deployments in classified environments. Collaborates with mission users, analysts, developers, and leadership to translate operational workflows into reliable, secure capabilities. Requires Python experience, TS/SCI clearance with polygraph, and relevant AI/ML or software engineering experience.

Optimize machine-learning inference for latency, throughput, and cost. The role profiles pipelines, improves kernels, batching, quantization, compilation, serving, and hardware utilization, while building serving and evaluation infrastructure. It partners with research and product teams to make models affordable at scale and treats compute cost as a core performance metric.

Own the architecture and hands-on implementation of production MLOps systems, scalable inference services, model deployment pipelines, monitoring, and governance. Integrate machine learning into pharmaceutical manufacturing, process control, automation, and scientific or clinical applications. Collaborate with chemical engineers, computational biologists, and platform teams while establishing enterprise CI/CD and MLOps standards. This is a hands-on senior role requiring hybrid onsite work in Indianapolis.

Build and maintain AWS-based machine learning platform infrastructure supporting model training, evaluation, deployment, annotation, and multi-tenant data management. Develop full-stack tools, optimize deep learning models for training and inference, implement containerized CI/CD workflows, and improve cloud cost efficiency. The role focuses on vision-based machine learning, edge deployment, serverless architecture, and scalable production pipelines while collaborating with machine learning scientists and cloud engineering teams.

Develop and optimize deep learning models for computer vision applications, focusing on neural network architecture, training, inference optimization, and production deployment. Build tools and infrastructure for model training, evaluation, and deployment in Linux and cloud environments. Research emerging machine learning techniques, integrate them into products, and collaborate with software engineers on customer-focused projects throughout the product lifecycle.

Develop ML and multimodal AI systems that integrate neurophysiological, behavioral, and sensor data for cognitive state estimation and decision support. Build algorithms and prototypes, apply cognitive science to improve explainability and human-AI collaboration, contribute to proposals/publications, and demonstrate capabilities for mission-driven R&D.

As a Senior Machine Learning Engineer, you will manage the entire lifecycle of safety-critical ML perception models, optimize performance, and ensure reliable deployment on embedded systems.

Build and own fraud metrics, dashboards, and reporting. Design and implement real-time monitoring, alerts, and predictive ML-driven risk controls. Balance loss savings with customer friction, run experiments (A/B tests), partner with GTM teams, summarize findings to stakeholders, and assist teams with fraud triage.

Design, scale, and maintain robust machine learning systems for AI-powered physical machines. Help define coding standards and the technology stack, work across software and hardware, solve ambiguous problems, and mentor new hires. Relevant work may include object recognition, reinforcement learning, and deep neural networks in a fast-growing AI and robotics startup.

Develop and deploy machine learning solutions for construction-site understanding using computer vision, multimodal AI, VLMs, LLMs, generative AI, RAG, and agent architectures. Curate high-quality datasets, optimize models for performance across hardware targets, and build production ML pipelines in the cloud. Own components from design through deployment and monitoring while collaborating in an agile team.

Build and operate machine learning models powering roleplay, scoring, and coaching products. Own the full lifecycle from training and fine-tuning through production deployment and maintenance. Fine-tune open-source models, optimize them for cost and latency, deploy them on-device when needed, and develop evaluation frameworks, benchmarks, and regression suites. Collaborate closely with founders and engineers to shape product direction.

Develop and integrate AI/ML capabilities into operational software, including large language models, retrieval-augmented generation, agentic workflows, prompt engineering, and inference pipelines. Evaluate, test, document, and communicate AI system performance, reliability, design decisions, and technical contributions. Preferred experience includes secure development, DevSecOps, Kubernetes, OpenShift, CI/CD, containerized delivery, and classified or regulated environments.

Design and build Robinhood’s agent platform, including orchestration, tool integrations, context and memory management, trajectory-level evaluations, simulation environments, and action guardrails. Ship production agentic applications, establish safety and evaluation standards, create reusable SDKs and CI gates, and provide technical leadership through architecture reviews, mentorship, and production-readiness decisions.

Own and evolve real-time machine learning detection systems for account compromise. Design, train, deploy, and monitor models in production at scale; architect systems for rapid growth; work with customers to understand attacks; and help establish engineering standards on a small, high-autonomy team.

Lead technical direction for high-impact machine learning initiatives, from research and experimentation through production deployment. Build and fine-tune LLMs, document understanding systems, model-serving infrastructure, evaluation frameworks, and data feedback loops. Establish standards for model quality, calibration, reproducibility, and responsible deployment while mentoring ML engineers and influencing organizational roadmaps. The role requires hands-on software and ML engineering expertise, production-scale model development, and strong statistical judgment.

Own and architect the core ranking system end-to-end: model design, production reliability, automated retraining, bias mitigation, personalization, and cross-functional technical leadership. Mentor engineers, drive roadmap, and scale experimentation and ML infrastructure.

As a Staff Machine Learning Engineer, lead teams in designing AI solutions, managing projects, and interfacing with clients while ensuring technical excellence and operational effectiveness.

Lead technical vision and architecture for ML-driven trajectory generation in autonomous vehicles. Build and deploy scalable training pipelines, integrate models into real-time safety-critical onboard systems, mentor senior engineers, drive cross-functional initiatives, and move solutions from research to production using simulation and large-scale datasets.

Lead research and development of emerging ML (embeddings, foundation models, transformers). Build and productionize machine learning models at scale, partner with product and engineering, analyze large-scale transactional and clickstream data, and run experiments to improve customer experiences across marketing, servicing, and fraud domains.

Own machine learning problems end to end, including data, modeling, evaluation, deployment, monitoring, and production optimization. Build training, inference, evaluation, datasets, and benchmarking pipelines for indoor positioning and item localization. Collaborate with applied scientists, engineering, and product teams to deliver enterprise features while optimizing accuracy, coverage, latency, and cost. Contribute reusable infrastructure, architecture decisions, and high engineering standards.