Top Machine Learning Engineer Jobs

Build end-to-end machine learning systems spanning research reproduction, model evaluation, inference infrastructure, backend services, and product interfaces. Design rigorous experiments, evaluation datasets, and verification workflows; analyze model internals; investigate provenance and evasion; and convert findings into reliable production systems with APIs, background jobs, observability, testing, documentation, and user-facing React/TypeScript experiences.

Develop and deploy production-grade machine learning pipelines, predictive models, and microservices for power-market forecasting and electricity-grid modeling. Translate research prototypes into scalable Python applications, manage PostgreSQL time-series and event data, implement MLOps practices, and build reliable ingestion and transformation pipelines. The role also involves testing, code reviews, CI/CD, Agile collaboration, and partnering with data science, data engineering, and product teams.

Support MLOps implementation and maintain machine learning models in production. Build scalable pipelines, develop production-quality services, implement unit and integration testing, manage CI/CD and DevOps practices, maintain APIs, improve go-to-production processes, and contribute across the software development lifecycle and data platform community.

Develop and maintain high-load, real-time personalization services for global e-commerce platforms. Improve shopper experiences and business KPIs through applied machine learning, user interaction modeling, personalization and recommender systems, hypothesis validation, and A/B testing. Collaborate with engineering teams and business stakeholders to research, deliver, and communicate personalization capabilities and outcomes.

Machine Learning Engineer Interns develop and deploy scalable machine learning algorithms, improve recommendation and affinity models, process large datasets, conduct offline and online evaluations, extract product attributes, and study fairness in AI-driven advertising. They collaborate with mentors, build prototypes, write production-ready code, and contribute to knowledge-sharing activities.

Design, develop, and maintain Python libraries and frameworks for machine learning developers. Architect scalable developer tools, shape APIs, grow early-stage open-source projects, create demos and documentation, and drive community adoption. Work on real-time audio/video streaming, networking, frontend integrations, and open-source collaboration through code reviews, discussions, and support.

Build, train and deploy large time-series foundation models (multimodal inputs), design reproducible experiments, create data exploration tools, add datasets, deploy models to API/platform, iterate on user feedback, and mentor future team members.

Six-month full-time internship to research and implement state-of-the-art computer vision and deep learning models, integrate CV with LLM applications, and collaborate with the ML engineering team to deploy models used by solar and roofing customers.

Develop and own production-ready machine learning, LLM, NLP, and RAG solutions within Palantir Foundry and AIP. Responsibilities include data exploration, model development, evaluation, deployment, monitoring, workflow integration, and optimization for accuracy, scalability, reliability, latency, and cost. Collaborate with engineers and business stakeholders, make architecture decisions, translate business needs into AI solutions, and communicate model behavior and limitations.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Design, build, optimize, deploy, and monitor production AI/ML solutions, especially LLM and VLM applications. Develop model integrations, APIs, MLOps pipelines, RAG systems, and scalable serving infrastructure. Apply software engineering, testing, CI/CD, observability, security, and compliance practices. Own model lifecycles from data preparation and research through production maintenance. Collaborate with engineers, project managers, clients, and stakeholders while mentoring junior engineers and evaluating emerging AI techniques.

Build and scale MLOps infrastructure that moves machine learning models from development to production. Responsibilities include model serving, automated training and deployment, CI/CD, monitoring, drift detection, rollback mechanisms, streaming feature pipelines, and agentic workflow integration. The role requires collaboration with data science, platform, and product teams to deliver reliable, observable, low-latency ML systems.

Own production optimization of LLM and VLM inference systems, improving latency, throughput, memory efficiency, GPU utilization, quality, reliability, and cost. Deploy and benchmark inference engines, build compression and quantization workflows, implement acceleration techniques, diagnose serving bottlenecks, and collaborate with kernel, platform, research, product, and customer teams.

Develops and validates AI/ML models for fiber test and measurement applications. Owns the end-to-end machine learning and signal-processing lifecycle, including data collection, preprocessing, feature extraction, model development, optimization, proof-of-concept and MVP validation, deployment, and production integration. Applies filtering, spectral analysis, noise characterization, statistical modeling, and time-series analysis to fiber network data while collaborating with international cross-functional teams.

Build and deploy production-ready Generative AI and LLM solutions across Aramis Group, including conversational agents, voice applications, RAG architectures, and integrations with APIs and vector databases. Develop AI pipelines using Python, SQL, Docker, and cloud technologies; monitor and optimize production systems; investigate incidents; evaluate technical solutions; and share findings with an international, multidisciplinary team.

Design, train, and evaluate large multimodal and vision-language-action models for robotic agents. Develop reinforcement and imitation learning methods, embodied datasets, simulation environments, world models, planning systems, and sim-to-real experiments. Build robust research software and distributed training infrastructure, validate approaches on physical robots, and collaborate with research and engineering teams to deploy reliable systems. Communicate findings through technical reports, open-source releases, demonstrations, and publications.

Designs, builds, and evaluates research prototypes and production-ready systems for portable AI agents, model routing, provider interoperability, memory, retrieval, tool use, and multi-agent workflows. Responsibilities include developing evaluation frameworks, conducting statistically rigorous experiments, creating schemas and APIs, optimizing model and agent systems, and translating research into secure, reliable, reusable components. The role collaborates across research, infrastructure, security, product, and engineering teams and communicates findings through technical reports, benchmarks, demonstrations, open-source releases, and publications.

Design and validate threat models for agentic AI systems, create adversarial experiments and synthetic environments, develop static and dynamic risk analysis, fine-tune and evaluate models, and build production-ready guardrails, monitoring, dashboards, and evaluation infrastructure. Optimize security systems for latency, cost, reliability, and measurable real-world performance while translating research into product capabilities.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Lead the development of computer vision models for healthcare robots, optimizing them for edge deployment and managing MLOps pipelines.

Design, train, deploy, and monitor scalable machine learning systems for player safety and security. Develop solutions using NLP, LLMs, anomaly detection, and graph learning; adapt research to production constraints; build high-quality software and MLOps pipelines; define success metrics; and collaborate with data and software engineers to deliver production models at scale.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

Own reliability for Vibe’s latency-sensitive prediction serving, bidding, pacing, and delivery infrastructure. Define SLOs, improve observability, reduce p99 latency and infrastructure costs, manage Redis and capacity planning, build end-to-end stress testing, and lead incident response and postmortems. Partner with data scientists and engineering teams to operate model-serving systems reliably at high scale.

Build foundational MLOps infrastructure and deploy multi-model CV and VLM systems for authentication and fraud detection. Design data/feature management, model tracking, serving, monitoring, and automated retraining pipelines. Scale ML capabilities for search, recommendations, pricing, and marketing while ensuring low-latency, high-throughput production services and strong engineering hygiene.

Distill and compress large vision foundation models into efficient detectors and re-identification modules for constrained edge and embedded hardware. Optimize models (quantization, pruning, LoRA), build and maintain MLOps training/evaluation pipelines, curate datasets, benchmark latency/performance, and collaborate with systems and mission teams to deploy robust solutions.