Top Senior Machine Learning Engineer Jobs in NYC, NY

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

Lead development of complex data pipelines, AI/ML systems, and software solutions. Provide technical leadership, mentor engineers, conduct code reviews, make architectural decisions, and collaborate with technical and non-technical stakeholders. Maintain hands-on Python development using scalable data engineering practices, cloud platforms, containerization, CI/CD, and machine learning frameworks. Experience with large language models and AI agents is highly desirable.

Own the full lifecycle of production classification and detection models, including transformer fine-tuning, entity detection, behavioral risk scoring, training, serving, versioning, evaluation, drift detection, and retraining. Build SageMaker-based ML systems and define output contracts supporting audit and policy enforcement. Lead the evaluation harness and mentor a co-op while operating as the company’s sole ML engineer.

Design and build production LLM infrastructure, including orchestration, prompting, workflow layers, retrieval integrations, MCP tools, evaluation systems, dashboards, and observability. Improve platform reliability through caching, incident response, scalability, and model rollbacks. Establish architecture standards, conduct design reviews, mentor engineers, and communicate AI concepts to nontechnical stakeholders.

Develop high-performance, large-scale machine learning services for a programmatic advertising platform. Build and deploy AI models and data-driven algorithms for real-time ad scoring, ranking, and bid optimization. Work with data scientists and cross-functional teams on scalable data analysis, model development, production rollouts, troubleshooting, and continuous optimization using agile methods.

Own the production lifecycle for machine learning and LLM-backed systems, including training and post-training pipelines, inference serving, model releases, monitoring, retrieval infrastructure, and cost and latency optimization. Partner with AI researchers and engineering teams to move prototypes into reliable production capabilities across customer tenants. Develop ETL and GraphQL features, enforce tenant isolation, and support deployments in regulated or customer-controlled environments.

Build and optimize batch and streaming data pipelines for AI, analytics, and enterprise platforms. Develop scalable transformations across cloud and lakehouse environments, implement data modeling, lineage, metadata, quality controls, and orchestration, and resolve complex performance and reliability issues. Partner with cross-functional teams to deliver secure, governed data solutions while mentoring engineers and leading modern data engineering initiatives.

Own the architecture, deployment, performance, and reliability of Stickerbox’s production machine learning pipeline. Optimize latency, cost, batching, caching, GPU utilization, model serving, and inference across image generation, speech recognition, and character models. Lead deployment strategies, capacity planning, observability, safety enforcement, automated quality checks, migrations, release engineering, and on-call operations for a global consumer hardware launch.

Build and scale AI/ML solutions for healthcare revenue cycle automation. The role owns AI outcomes, identifies high-value use cases, shapes technical and product roadmaps, converts prototypes into scalable production systems, and architects infrastructure for training, testing, monitoring, security, and deployment. The engineer will apply ML, NLP, and LLM approaches to claims and workflow optimization while balancing technical tradeoffs, business value, cost, and operational efficiency.

Leads the architecture, development, and optimization of production-grade machine learning infrastructure on Azure. Designs scalable training and inference environments, implements MLOps practices, automates ML workflows, and establishes reusable deployment frameworks. Partners with data science, DevOps, and platform engineering teams to productionize models, strengthen security and observability, and ensure reliable, compliant systems. Mentors junior engineers and drives ML infrastructure strategy across multiple use cases.

Design, build, and operate production-grade GenAI and RAG platforms at scale. Develop retrieval pipelines, evaluation frameworks, lifecycle management, CI/CD, observability, safety controls, cost monitoring, and incident workflows. Partner with product, platform, and governance teams to meet enterprise reliability, security, and compliance standards. Mentor engineers and establish best practices for scalable, trustworthy AI systems.

Contribute to machine learning model design, training, and evaluation for fraud detection. Communicate findings, develop metrics, and support production ML workflows while ensuring compliance and model governance.

Design and lead large-scale machine learning and backend systems for advertiser growth, including budget pacing algorithms, GenAI creative generation infrastructure, marketplace experimentation, high-throughput billing pipelines, and real-time stream processing. Analyze marketplace and financial data, optimize advertiser performance, ensure billing accuracy and fault tolerance, lead multi-quarter technical roadmaps, and mentor senior engineers.

Build production-grade AI and agentic systems for healthcare workflows. Responsibilities include designing data pipelines, evaluation harnesses, model deployment and monitoring systems, and scalable LLM- and vision-based applications. The role spans backend and full-stack development, AI agent orchestration, experimentation, training pipelines, and frontend integration while collaborating with ML researchers and engineering teams.

Design, build, and operate production agentic AI systems for enterprise clients. Develop autonomous agents using Glean, OpenAI/GPT Enterprise, and AWS or GCP. Own agent workflows from architecture and implementation through deployment, monitoring, evaluation, security, and iteration. Apply tool calling, RAG, multi-step orchestration, memory, prompt engineering, structured outputs, permission-aware retrieval, and cost and latency optimization. Measure reliability and quality using evaluations and maintain enterprise-scale production software.

Designs, evaluates, and improves ranking and recommendation systems through experimentation, feature engineering, and production machine learning. Builds reliable data and feature pipelines, incorporates multimodal signals, manages uncertainty, calibration, and model drift, and monitors system performance. Partners cross-functionally with Engineering, Product, Business, and Analytics to deliver scalable, latency-sensitive solutions with measurable business impact.

Develop and deploy machine learning models and production software for multi-object tracking in autonomous trucks. Responsibilities include sensor fusion across camera, LiDAR, and radar; scalable PyTorch training workflows; tracking algorithm development; large-scale data analysis; C++ and Python engineering; safety and validation strategy; architecture decisions; and cross-functional collaboration. The role also provides technical leadership through design reviews, code reviews, mentoring, and engineering best practices.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

Develop and optimize large-scale advertising ranking models for CTR and CVR prediction, calibration, user representations, and sequence modeling. Own machine learning systems end to end, including data pipelines, feature engineering, training, evaluation, deployment, and maintenance. Collaborate with platform, product, data science, and engineering teams to run experiments and improve advertiser outcomes, revenue, and user relevance. Apply deep learning and recommendation techniques within production latency, reliability, and cost constraints.

Design, train, deploy, monitor, and optimize speech and NLP models for a voice-agent platform. Build MLOps pipelines, APIs, and inference services while developing Android and iOS applications that integrate AI, voice, real-time communication, and backend services. Create frontend prototypes and dashboards, conduct model experiments, evaluate vendors, optimize performance, document systems, and maintain engineering quality across collaborative teams.

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.

Lead the design, training, evaluation, and deployment of large-scale machine learning models, including LLMs, diffusion models, and GNNs. Own distributed training pipelines, dataset interfaces, optimization strategies, model evaluation, serving, and lifecycle management. Collaborate with data, infrastructure, and scientific teams while providing technical leadership through architecture reviews, mentorship, and cross-functional decision-making.

As a Senior AI/ML Engineer, you will manage the AI/ML lifecycle, develop predictive models, conduct anomaly detection, and integrate AI capabilities into the Confido platform.

Build and operate large-scale batch and streaming data pipelines and real-time feature infrastructure to support production ML. Develop ML-adjacent services, ensure reliability/observability, participate in on-call and incident response, and collaborate with ML, data, and platform teams to meet latency, availability, and data quality requirements.

Build and deploy production machine-learning systems within client environments, covering data discovery, modeling, validation, deployment, scheduling, monitoring, and retraining. Develop forecasting and detection models for imperfect time-series data, establish honest evaluation and KPI frameworks, and ship usable APIs or interfaces. Work directly with client IT and data teams through discovery, demos, and feedback cycles. The role also involves data engineering, software development, basic cloud and DevOps work, and advising clients when ML is not appropriate.