Top Machine Learning Engineer Jobs

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.

As an AI Engineer, you will develop and optimize AI systems for legal tasks, work on integrating models, and establish evaluation frameworks to enhance decision-making capabilities in complex legal workflows.

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 Staff AI/ML Engineer, you'll enhance developer productivity, mentor engineers, drive best practices, and implement scalable solutions for AV tools and services.

The Principal AI/ML Engineer will lead the AV ML Infra team, overseeing scalable ML infrastructure, project ownership, collaboration, and team mentorship in AI and ML innovation at GM.

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.

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.

Own end-to-end machine learning for robotic grasping and perception, including problem formulation, large-scale dataset generation and curation, model training, experimentation, evaluation, and verification. Develop vision models using synthetic data, self-supervised labeling, and active learning. Define metrics tied to robot performance, produce deployable model artifacts, conduct code reviews, mentor engineers, and contribute to the perception and grasping roadmap while partnering on runtime integration.

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.

Lead DigitalOcean’s AI security strategy, governance framework, threat modeling, and multi-year roadmap. Partner with security, engineering, and data science teams to secure AI products, deploy guardrails, conduct adversarial testing and red teaming, and monitor AI/ML systems. Provide architectural reviews, executive risk briefings, and guidance on emerging threats including prompt injection, model supply-chain risks, agentic AI, and data exfiltration.

Lead DigitalOcean’s AI security strategy, governance framework, roadmap, and organizational programs. Build and operationalize AI/ML security controls, tooling, guardrails, and automation; conduct threat modeling, adversarial testing, and AI red teaming. Review AI architectures, guide high-risk product decisions, monitor deployed models, and address risks involving RAG, agents, model supply chains, and prompt injection. Represent AI security to executives and external communities while translating emerging research and threats into engineering guidance and policy.

Lead DigitalOcean’s AI security strategy, governance framework, and multi-year roadmap. Secure AI/ML systems across data ingestion, training, model serving, RAG, agents, and monitoring. Conduct threat modeling, adversarial testing, AI red teaming, and security reviews; develop guardrails and automation with engineering teams. Advise executives and the board on AI risk, define organizational controls, and guide responses to emerging threats including prompt injection, model supply-chain risks, and AI-enabled social engineering.

Lead DigitalOcean’s AI security strategy, governance framework, and multi-year roadmap. Secure AI/ML systems across data ingestion, training, model serving, RAG, agents, and monitoring. Conduct AI red teaming, threat modeling, security reviews, and adversarial testing, while building defensive tooling and guardrails. Advise executives and the board on AI risk, guide engineering teams, and define organizational policies for responsible AI security.

Lead DigitalOcean’s AI/ML security strategy, architecture, governance, and multi-year roadmap. Build security controls, guardrails, tooling, and automations; oversee AI red teaming, adversarial testing, threat modeling, and model monitoring. Review AI product architectures, guide high-risk decisions, establish responsible AI policies, and brief executives and the board on AI security risks. Serve as the internal expert and external representative on emerging AI security topics, including agentic AI, model supply chains, prompt injection, and data integrity.

Leads the design, development, deployment, and optimization of agentic AI, LLM, machine learning, and propensity modeling solutions. Builds models for customer behavior, churn, and lifecycle triggers; manages end-to-end MLOps pipelines; consolidates models; monitors performance; and provides technical mentorship. Presents model insights and strategic recommendations to senior leaders and business partners while driving hyper-personalized customer experiences.

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.

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.

Leads software engineering for a Machine Learning I/O team, developing secure production code and guiding design, testing, debugging, and operational stability. Drives enterprise adoption and governance of AI-assisted development tools, establishes validation standards for AI outputs, promotes secure and responsible AI practices, and influences technical decisions across teams. Provides technical direction, serves as a subject matter expert, and advocates for scalable cloud-native engineering practices.

Designs and maintains production MLOps and LLMOps pipelines across Azure and Databricks environments. Responsibilities include model training, deployment, monitoring, retraining, versioning, CI/CD automation, infrastructure provisioning, model serving, testing, governance, and responsible AI controls. The role partners with data scientists, engineers, security, and governance teams to operationalize reliable, auditable machine learning solutions and troubleshoot production issues.

Own and scale infrastructure for training and serving machine learning models in production. Responsibilities include improving prediction latency, stability, observability, and uptime; building training environments; developing monitoring and data pipelines; and designing multi-cloud systems that balance performance and cost. The role requires Python, Kubernetes, GPU performance tuning, model deployment, and production operations experience in a fast-paced startup environment.

As a Founding Engineer, you'll manage the lifecycle of AI systems, optimizing performance and developing pipelines for biopharma marketing.

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.

Lead technical direction and people management for an AI/ML engineering squad. Design, build, and deploy production-grade ML and Generative AI solutions (LLMs, RAG, agentic systems), drive MLOps/CI-CD, ensure Responsible AI and HIPAA-compliant practices, conduct architecture/code reviews, and mentor engineers to deliver measurable business outcomes.

Lead design, implementation, and scaling of end-to-end ML systems including feature pipelines, model training, evaluation, deployment, and monitoring. Partner with cross-functional teams to productionize models, apply MLOps best practices (automated retraining, CI/CD, drift detection), and mentor engineers to maintain high code and system quality.