Top Remote Machine Learning Engineer Jobs

Lead architecture and technical strategy for AI-driven product quality systems using LLMs and agents. Build scalable evaluation frameworks, detect regressions, generate insights, and drive cross-functional adoption while mentoring engineers and defining standards for trustworthy AI.

Design, build, and operate scalable hybrid human/machine data-labeling tools and pipelines for autonomous vehicle ML. Implement full‑stack features (frontend, backend, APIs), integrate ML-driven annotation and active learning, build automation and QA tooling, and own projects end-to-end while collaborating with ML, product, and operations teams to improve labeling velocity and quality.

Design, build, and operate scalable hybrid human/machine data labeling platforms for autonomous vehicles. Own end-to-end projects, define platform roadmap, integrate ML-driven annotation, build high-performance UIs/APIs, and collaborate across ML, product, and operations to improve labeling quality and velocity.

Build and operate full‑stack labeling tools and pipelines for autonomous vehicle ML. Design scalable UIs and services, integrate ML-driven annotation, create automation and QA tooling, own projects end‑to‑end, and collaborate with ML, Ops, Product, and Data teams to improve labeling quality and velocity.

Design, implement, benchmark, and optimize CUDA-based GPU kernels and custom operators for on-vehicle ML inference. Build tooling for profiling and debugging, integrate kernels into the ML runtime, collaborate with compilers and architecture teams, and deliver high-performance, production-grade libraries meeting strict latency, throughput, and reliability targets.

As a Principal Machine Learning Systems Engineer, you will lead the development and implementation of machine learning systems and solutions to enhance team collaboration and productivity across Atlassian's software products.

Design and productionize ML models and agentic systems for intraday financial alpha generation. Build data pipelines, experiment frameworks, and portfolio optimization methods. Collaborate with quants and engineers to move models from research to live trading environments.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

Design, build, and operate scalable, low-latency search and retrieval systems powering Atlassian products. Develop and serve production ML models (neural rankers, embeddings, rerankers) using PyTorch and Triton. Implement vector indexing and RAG/agentic retrieval pipelines, ensure observability and cost optimization, manage on-call/runbooks, and collaborate cross-functionally while mentoring junior engineers.

Design, deploy, and maintain end-to-end ML risk solutions at scale to detect and prevent fraud, merchant risk, and credit loss. Partner with cross-functional teams, lead technical decisions, build ML tooling, monitor models in production, and investigate emerging abuse patterns to improve detection and decisioning.

Lead design and deployment of scalable ML platforms and tools, manage complex technical projects, mentor junior engineers, and collaborate across teams to enhance autonomous driving systems.

Join a talent network connecting Principal AI/ML Engineers with VC-backed startups. Responsibilities include architecting and deploying ML/DL models, researching state-of-the-art AI (LLMs, transformers, RL), building scalable training and inference pipelines, leading AI strategy and technical teams, ensuring robust MLOps, optimizing models for production, and collaborating across engineering, data, and product.

Join a talent network connecting Principal AI/ML Engineers with VC-backed startups. Responsibilities include architecting and deploying production ML/DL models, researching state-of-the-art methods (LLMs, transformers, RL), building scalable training/inference pipelines, implementing robust MLOps, optimizing model performance, and leading technical strategy and teams.

Design, build, and deploy autonomous AI agents using LLMs and agent frameworks; integrate agents into e-commerce and supply chain systems; implement multi-agent orchestration, memory and tool integrations; fine-tune models and apply RL concepts; and build safety, monitoring, and MLOps pipelines.

Design, build, and maintain scalable AI/ML systems and ETL pipelines for cloud operations. Develop AI agents, predictive models (drift and cost anomaly detection), and automations; integrate ML insights with AWS/GCP/Azure; optimize data storage (Snowflake) and querying (Athena); orchestrate workflows with Airflow; conduct code reviews, mentor teammates, and evaluate emerging AI technologies for operational improvements.

Design, build, deploy, and operate production-grade AI/ML and LLM systems (including RAG and agentic pipelines). Integrate AI capabilities with product workflows, run evaluation and monitoring, optimize model and system performance, and apply strong engineering practices (testing, CI/CD, versioning).

Design, develop, fine-tune, and deploy generative AI models (GANs, VAEs, transformers). Collaborate with cross-functional teams, troubleshoot model issues, document work, and communicate technical concepts to non-technical stakeholders while staying current on research.

Design, develop, and deploy machine learning and deep learning models; build end-to-end ML pipelines including preprocessing, training, evaluation, and deployment; perform feature engineering on large datasets; deploy and monitor models on cloud platforms; collaborate with cross-functional teams to integrate AI solutions and retrain models as needed.

Design, build, and deploy production-grade ML and AI systems end-to-end, combining classical ML, deep learning, and LLM/agentic integrations. Implement MLOps, monitoring, CI/CD, model serving, vector search/RAG pipelines, and scalable APIs; mentor engineers and drive engineering best practices.

Design, deploy, and optimize cloud-native ML systems on AWS, integrate foundation models (Bedrock/Anthropic), build and monitor SageMaker pipelines and data pipelines, detect drift, extend ML infra and libraries, and collaborate cross-functionally to deliver secure, cost-efficient production ML services.

Design, develop, and deploy end-to-end machine learning models using statistical and ML algorithms. Prepare and enrich data, implement scalable PySpark/Databricks solutions, and collaborate with product and engineering to create personalized customer experiences and actionable insights.

Design, develop, and deploy production 3D and BEV perception ML models for autonomous trucks. Own end-to-end model lifecycle from data prep, training, evaluation, and deployment. Improve model robustness, performance, and generalization across sensor modalities. Integrate models with autonomy software, enhance training pipelines and tooling, analyze failures, and mentor junior engineers.

Design, build, and operate large-scale ML systems across data, training, evaluation, inference, and deployment. Lead GPU-optimized training and inference pipelines, ensure reliability, performance, safety, and seamless integration into products. Own production deployments, monitoring, and cross-functional best practices to drive measurable model improvements and efficient research-to-production cycles.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Lead production-grade ML systems: build data pipelines, training and evaluation workflows, and scalable GPU inference. Fine-tune and adapt large models (LoRA, QLoRA, SFT, DPO, distillation), optimize deployments for latency/cost/reliability, and collaborate with product and engineering to ship robust, observable model-driven features.