Top Machine Learning Jobs

Build, ship, and own end-to-end ML models for vendor matching, order routing, and supply-chain optimization. Partner with data engineering to productionize models, quantify impact, document work, and implement decision logic components toward agentic AI in a fast-moving healthcare startup environment.

Develop and advance ML and signal-processing algorithms for SAR-based Automatic Target Recognition (ATR). Perform R&D on remotely sensed RF data, create Near Real Time algorithms, prototype NRT systems, and contribute to analysis reports and briefings supporting physics-based signature estimation.

The ML Engineer will design, develop, and implement machine learning models, manage ML pipelines, integrate APIs, and collaborate with teams to deploy solutions.

Lead architect and hands-on developer for embedded AI/ML autonomy across the full lifecycle: model design, optimization for resource-constrained flight hardware, integration with flight software and avionics, V&V and runtime assurance, cross-functional alignment, and customer-facing technical leadership for mission-critical space systems.

Develop and innovate machine learning algorithms to maximize advertising ROI, prototype and test models on historical data, and implement production-ready code in collaboration with Data Engineers to scale solutions across a high-throughput programmatic advertising platform.

Design, prototype, validate, deploy, and monitor end-to-end machine learning solutions for Wealth Management. Collaborate with MLOps, validation, Risk/Legal/Compliance, and business stakeholders; run experiments and A/B tests; present modeling results and ensure production reliability.

Lead development of internal Python SDKs and agentic AI workflows, build scalable FastAPI ML services, maintain CI/CD with Jenkins/GitLab Runners, and use Galileo for model evaluation and observability to productionize autonomous systems.

Build and scale LLM-based, agentic customer-engagement systems: architect, deploy, and monitor production ML features; finetune models; create evaluation frameworks, feedback loops and HITL workflows; run A/B tests; optimize latency, cost, and reliability; and mentor the team.

Hands-on engineering role building AI-enabled systems: develop, test, and deploy ML models, data pipelines, backend services, APIs and cloud-based applications. Support model training, evaluation, integrations, CI/CD, and production readiness while following technical direction from the Technical Manager and collaborating with engineers and stakeholders.

Lead design and delivery of end-to-end ML solutions for Wealth Management, build and deploy scalable models (recommender systems, GNNs, LLMs), run large experiments, validate and monitor production models, partner with cross-functional teams, and mentor data scientists to drive measurable business outcomes.

As a Founding ML Engineer, you'll develop ML models for lead optimization, automate demand generation processes, build data pipelines, and collaborate with teams to enhance growth through AI solutions.

Lead end-to-end development and deployment of ML credit models (collections, scoring, fraud, segmentation). Provide technical leadership, validate and document models, manage validation queries, collaborate with stakeholders and implementation teams, mentor junior scientists, and drive ML innovation and production monitoring in banking/fintech.

Lead the design and operation of analytical and machine-learning systems, define AI/ML strategy, and ensure model reliability and compliance in production.

Lead architecture and maintenance of end-to-end traffic classification ML systems for adversarial domains. Own model lifecycle from offline evaluation to millisecond-edge deployment, build certified offline-to-online data pipelines, establish rigorous evaluation and leakage-prevention, optimize inference under strict latency/cost budgets, integrate scoring with mitigation workflows, and provide cross-functional leadership and mentorship.

Lead development and productionization of LLM quantization and deployment pipelines (PTQ, QAT, mixed-precision, INT8/FP4), build robust export/calibration/benchmarking/validation tooling, collaborate with research, field-testing, simulation and vehicle software teams, curate evaluation datasets and metrics, analyze numerical errors and performance trade-offs, and develop orchestration workflows for on-vehicle inference and model sign-off.

Design and scale data pipelines and automated closed-loop systems for continuous improvement of perception models. Deploy active learning, curate and prune large datasets, develop evaluation metrics and ground-truth‑free performance measures, and build robust mining, training, and evaluation pipelines for rare, safety-critical scenarios while collaborating with research and operations teams.

Work on training, evaluating, and deploying ML models and inference systems for low-payload robotic arms. Run experiments, analyze results, and support testing and deployment on real robotic hardware while collaborating with researchers, engineers, and hardware teams.

Lead the development of machine learning methods for AI-native metal manufacturing systems, focusing on integrating physical data with physics-based simulation.

Seeking a Machine Learning Engineer to apply AI technologies in high frequency trading, focusing on automation and collaboration with AI research.

Own end-to-end matching and ranking systems for out-of-home advertising: design candidate generation and re-ranking models, build and manage data warehouse and pipelines, implement evaluation and monitoring, publish low-latency ranking APIs, and maintain model observability and retraining workflows.

You will design and develop machine learning systems for an AI-driven dating platform, focusing on personalized matching algorithms and natural language interactions.

The Senior ML Systems Engineer will build and maintain the training framework for large-scale language models, focusing on distributed training and performance optimization.

The Senior Machine Learning Engineer will automate healthcare compliance workflows using AI to process unstructured data, build multimodal AI pipelines, and develop extraction systems, ensuring high reliability and operational efficiency.

Design, train, and deploy reinforcement learning systems for high-impact decision problems. Build and evaluate RL policies at scale, handle reward modeling and simulation, and productionize stable, safe RL solutions.

The role involves designing de novo frameworks for molecular engineering, collaborating across teams to optimize data-driven biological experiments and integrating ML models with experimental systems.