Top Machine Learning Jobs

Design, build and deploy production AI/ML solutions from complex, distributed farm data. Engineer features, select models, run experiments, move models to scalable real-time production, monitor and observe AI systems, evaluate emerging AI technologies, and collaborate with product, engineering and domain teams. Contribute to user-facing features with React/TypeScript as needed.

Design, build, and deploy production-grade AI/ML solutions that convert complex, distributed farm data into actionable intelligence. Engineer features, select models, run experiments, and move models to scalable, real-time production. Integrate, fine-tune, monitor, and observe models on cloud infrastructure (AWS, Databricks). Partner with product, engineering and domain teams and contribute to user-facing experiences when needed.

Build and productionize agentic AI systems that retrieve and synthesize structured and unstructured financial data. Work across the ML lifecycle: experiment, deploy, monitor, and evaluate agents and retrieval methods. Collaborate with data, product, design, and MLOps teams to create scalable, robust solutions that connect data to business insights.

Design, develop, and deploy AI/ML models and pipelines for chemical and biological threat characterization. Build data preprocessing, embedding, entity extraction, and model monitoring systems; integrate models into secure cloud environments via APIs and MCP. Create model cards, explainability tools, and support knowledge graphs. Ensure DoD cybersecurity compliance, participate in Agile teams, and assist knowledge transfer and documentation.

Design, prototype, and ship LLM- and agent-powered AI features for observability. Rapidly iterate with real users, integrate AI workflows with tooling and alerting, collaborate cross-functionally, and ensure scalable, production-ready solutions in cloud-native environments.

Design and implement advanced ML models (reinforcement learning, neural nets, metaheuristics) for logistics optimization, predictive analytics, anomaly detection, and real-time decision systems. Lead architecture design for scalable, secure AI systems, mentor teams, align technical roadmaps with defense requirements, and conduct feasibility studies and technology evaluations for contested and uncertain logistics environments.

Develop and deliver production-ready 3D and camera-based perception ML models (BEV emphasis). Own model training, evaluation, debugging, and integration into autonomy systems. Improve training pipelines, tooling, and robustness; lead small technical initiatives and mentor junior ML engineers while collaborating cross-functionally to deploy perception models at scale.

Develop software and machine learning algorithms for human-computer interface products. Design LLM-based system architectures for conversational, retrieval, and automation tasks. Use data science and statistics to analyze and optimize model performance. Build and optimize systems for latency, reliability, and cost for real-time ML applications.

Take end-to-end ownership of ML problems from framing and experimentation through deployment and support in customer environments. Build, deploy, and maintain production ML models that inform engineering decisions, and communicate technical tradeoffs to cross-functional and non-technical stakeholders.

Build and evaluate next-generation ranking and recommendation models using LLMs, RLHF, and preference learning. Develop large-scale user understanding systems, apply RL and optimization to bidding and delivery, design experiments with causal inference and A/B testing, and collaborate with engineering to deploy research into production while communicating results to technical and non-technical stakeholders.

Design and scale real-time bidding systems and bid forecasting; develop ML, reinforcement learning, and control-based bidding and pacing algorithms; analyze marketplace dynamics; run offline evaluations and online A/B experiments; collaborate with product, infrastructure, and engineering teams to deliver measurable advertiser ROI.

Build and maintain large-scale user understanding, conversion prediction, and ad ranking models. Monitor and calibrate model performance, address data drift and delayed feedback, and improve recommendation and bidding strategies. Set long-term technical direction with other leads and communicate complex technical concepts to non-engineering stakeholders.

Design, optimize, and scale training and inference workloads for personalization and continual learning. Productionize research prototypes, improve latency/reliability/efficiency, optimize per-user serving and memory retrieval, and work on distributed training across GPUs and nodes. Collaborate with researchers, platform engineers, and customers; establish engineering practices, testing, on-call, and security posture.

Design, build, and maintain end-to-end ML and data solutions: analyze and preprocess client data, implement ML and deep learning models, deploy LLM/GenAI solutions, build data pipelines, monitor model performance, integrate APIs, collaborate with cross-functional teams, and communicate insights to stakeholders.

Lead development of scalable AI/ML capabilities for Mail Intelligence: build LLM- and ML-based personalization, data pipelines, tools, and observability. Mentor engineers, collaborate cross-functionally, run A/B tests, implement feedback loops, and balance cost/quality/speed tradeoffs to deliver production-grade solutions.

Design, build, and deploy scalable distributed deep learning pipelines and infrastructure. Optimize hardware utilization, profile and benchmark training, manage large datasets, and enable reproducible ML workflows in cloud environments. Collaborate with ML scientists and engineers to align pipelines with research goals and drive performance, reliability, and operational best practices for large-scale model development.

Paid 3-month remote contract for students or recent grads to build and test LLM-based solutions using the Token Factory inference platform. Assist senior SAs with prompt engineering, model selection, benchmarking, and inference optimization. Run performance and quality experiments and contribute to internal tooling and automation. Mentorship provided; strong performers may be considered for full-time roles.

Design, build, and productionize large-scale ML models (CTR, retrieval, conversion, recommendation, search). Lead end-to-end experimentation and online A/B testing, improve model serving and pipelines for low-latency scalable inference, translate product requirements into ML solutions, and mentor/lead other ML engineers.

Design, develop, deploy, and monitor machine learning and deep learning models (CNNs, RNNs, Transformers) for credit risk, fraud, and repayment prediction. Work with large datasets using SQL/BigQuery or HiveQL, perform statistical analysis and experiments, build features, create visualizations, and collaborate with ML scientists and engineers to productionize models while ensuring regulatory compliance and operational risk controls.

Lead strategy, development, and production deployment of scalable AI/GenAI and ML solutions. Drive technical direction, mentor data scientists, own end-to-end initiatives, ensure MLOps and model governance, collaborate cross-functionally, and deliver measurable business impact using advanced models (LLMs, transformers, diffusion) and cloud platforms.

Design, build, and deploy production-ready machine learning models and inference pipelines for enterprise AI use cases. Collaborate with data science, MLOps, and engineering teams to prepare data, implement model training and inference, optimize performance, ensure privacy and responsible AI, write automated tests and documentation, and support model monitoring, troubleshooting, and production integration.

Design, develop, and maintain AI/ML software solutions; collaborate with engineers and leadership; build tools to analyze large-scale data; debug, optimize, test, and document software; leverage modern LLM tools to accelerate development and improve code quality.

Lead design and deployment of offline 3D reconstruction, lane-line detection, and automatic mapping pipelines from multi-sensor logged data. Develop pose estimation, sensor fusion, and perception models; implement MLOps, data ingestion/curation, and quality measurement. Serve as project lead, mentor engineers, and define analytics and ML standards to scale auto-labeling and map creation.

Design, develop, test, and productize AI/ML algorithms: select and process data, run statistical analysis and ML models, perform test & evaluation and V&V, integrate systems, retrain models, verify model quality, and convert prototypes into scalable solutions.

The Machine Learning Engineer will design, build, and maintain scalable ML systems, optimize ML pipelines, implement MLOps, and collaborate with teams to deploy models into real-time products.