Top AI & Machine Learning Jobs

The role involves developing scalable ML algorithms, optimizing LLM inference performance, managing model serving pipelines, and collaborating with data engineers for efficient knowledge graph generation.

The Research Engineer will focus on multimodal sensing systems and sensor fusion for embodied AI and robotics, evaluating sensor modalities and collaborating with hardware and ML teams to enhance robotic learning and performance.

Build and maintain Bridgers shared technology platform through full-stack development and AI agent development for workflow automation. Work closely with end users in-person in the NYC office, take ownership of projects from conception to production, and collaborate with operational teams to improve workflows.

The AI Strategist will lead enterprise customers in SEO and AI search strategies, driving measurable outcomes and collaborating closely with product and R&D teams to create impactful narratives.

Lead end-to-end AI-first transformation for operations: define problems, design architecture, build agentic systems and automation pipelines, integrate with CRMs and operational tooling, ship production solutions and drive company-wide AI adoption.

Lead product marketing and go-to-market for vLLM: own conferences, partner events, landing pages, speaker coordination, collateral, and community programs. Shape messaging, build partner relationships, and execute developer-focused campaigns in collaboration with product, engineering, design, and leadership.

The Data Scientist supports customer use of the Epistemix platform through data integration, modeling, and visualization, improving decision-making and customer satisfaction.

Lead design and implementation of production ML systems for low-latency ad use cases (bidding, ranking, pacing, fraud). Build training and feature pipelines, real-time inference services, and ML team. Partner with engineering and product, own model experimentation, evaluation, monitoring, and deployment at scale.

Work with the quant team to build components of a systematic investment platform: data ingestion with point-in-time correctness, factor and risk model development, research-to-production tooling, backtesting/simulation, feature store, and portfolio optimization. Internship emphasizes robust, out-of-sample-valid research and software engineering.

Design, train, and deploy custom deep learning models for radar and camera (EO/IR) semantic segmentation, detection, and temporal tracking. Manage dataset design, collection, synthetic augmentation, and ML-Ops pipelines. Optimize models for resource-constrained edge hardware and collaborate with fusion/autonomy engineers for integration into multi-target tracking and SLAM systems.