Solutions Consulting Manager in New York

We are investing in agentic AI and need a Senior AI Engineer to lead the design and delivery of these systems. This is a foundational hire: you will own both the agent-facing workstreams — pipelines, orchestration, conversational interfaces — and the underlying context layer that makes them reliable, including memory management, knowledge graph integration, and retrieval infrastructure. You will work closely with data engineers, project leads, and client stakeholders, and play a key role in shaping how Lynx builds and ships AI solutions at scale. What This Involves: Lead the architecture and delivery of agentic AI systems end-to-end: agents, orchestration, tool use, and multi-step reasoning workflows. Own the context layer: design and implement memory architectures (episodic, semantic, working memory) and integrate GraphRAG and knowledge graph retrieval into agentic pipelines. Build robust RAG systems — including vector retrieval, graph traversal, and hybrid search — and ensure retrieval quality through evaluation frameworks. Translate client requirements into technical designs, presenting approaches and trade-offs to both technical and non-technical stakeholders. Define standards and reusable patterns for agentic AI development that other engineers at Lynx can build on. Set up observability, evaluation, and monitoring pipelines to ensure AI systems perform correctly in production. Requirements: 5–8 years of software or ML engineering experience, with at least 2–3 years building LLM-based or agentic AI systems in production. Deep hands-on experience with agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI, or similar) and LLM APIs (OpenAI, Anthropic, etc.). Strong understanding of agent design patterns: ReAct, planning loops, tool use, multi-agent coordination, and memory architectures. Practical experience with GraphRAG or knowledge graph-based retrieval (e.g., Neo4j, Microsoft GraphRAG) and vector databases (Pinecone, Weaviate, Qdrant, etc.). Proficiency in

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. Fraud Data is the data science and machine learning team within Plaid’s Fraud organization, responsible for using data and ML to improve and scale Plaid’s fraud products. Within Fraud Data, the Customer & Product Intelligence team focuses on understanding product performance, uncovering customer insights, and enabling go-to-market teams with data-driven solutions. The team partners closely with customers and GTM teams on fraud analyses and proofs of concept, turning customer learnings into scalable, reusable product capabilities. We also build the metrics, analytics, and data foundations that measure product health, identify opportunities for improvement, and guide product decisions across Plaid’s Fraud portfolio. As a Data Science Manager, you will lead a team responsible for customer-facing data science and Fraud product analytics. You will set the team's roadmap, develop its data scientists, and remain involved in analytical methods, technical reviews, and customer investigations. You will: Set a 6–12-month roadmap with Product, Engineering, and GTM, and assign priorities and responsibilities across the team. Define product metrics, their underlying data, and reporting and

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. With a billion rides per year and counting, Lyft is solving hard problems in a rapidly growing domain with a lot of data and creative solutions in Rider, Driver, Marketplace, and beyond. While traditional approaches to optimization and problem decomposition are sufficient to disrupt transportation, building a next-generation platform for low-cost, ultra-immersive transportation to improve people's lives warrants modern ML utilizing petabyte-scale data. Our highly motivated Machine Learning Engineers work on these challenging problems and define solutions to directly impact various aspects of our core business. The Fulfillment group, within the Marketplace at Lyft, is responsible for determining what inventory can be reliably offered for a given rider session and fulfilling rider requests. The group comprises several sub-teams that generate feasible offers for riders, match rider requests with drivers, and maintain a distributed state machine to track rides and drivers from request through completion. We are seeking a Machine Learning Engineer to join the Fulfillment team and lead the design, development, and deployment of state-of-the-art machine learning systems. This role requires a strategic thinker who can balance high-level system architecture with hands-on technical implementation. You will collaborate across teams to shape the future of ride-sharing by leveraging machine learning and data science. Responsibilities: Design, build, and deploy machine learning models for real-time applications, including translating state-of-the-art research into production-ready solutions Design and implement feature pipelines, model training workflows, and serving infrastructure using Lyft's ML platform Evaluate ML system performance against business KPIs, run experiments, and drive continuous model improvement

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. At Plaid, our mission is to empower innovators by delivering access to the financial system. Since 2012 we've helped tens of millions of consumers live a better financial life and thousands of customers build a new financial future. We work with companies like PayPal, Rocket, H&R Block, and many of the largest banks in the US to make it easy for consumers to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Raleigh, Washington D.C., London and Amsterdam. The Account Management team in Plaid’s Enterprise Segment leverages deep product and industry expertise to help our customers build and scale technology solutions. We act as partners, advisors, and advocates for our customers to help shape how consumers interact with their finances. As an Enterprise Account Management Lead, you’ll be responsible for managing the team of senior account managers who are growing the revenue from the Enterprise customer base, as well as setting the vision and strategy to build long-term

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. We are a visionary and innovative industry expert team with a track record of architecting, building, and taking solutions to market applicable to various sub-verticals across financial services, spanning retail and commercial banking, lending and payments. There is only one Data Cloud. Snowflake's founders started from scratch and designed a data platform built for the cloud that is effective, affordable, and accessible to all data users. But it didn't stop there. They engineered Snowflake to power the Data Cloud, where thousands of organizations unlock the value of their data with near-unlimited scale, concurrency, and performance. This is our vision: a world with endless insights to tackle the challenges and opportunities of today and reveal the possibilities of tomorrow. Are you passionate about taking new ideas and innovation to market? Have you successfully applied technology to solve business problems in core banking? If so, we have a strategic opportunity to use your expertise across Data, Cloud, and AI to drive transformational changes across the financial services industry in a highly visible role collaborating with internal and external leadership. Snowflake is seeking a seasoned Financial Services Industry Architect with deep, hands-on experience designing and d

The Applied AI team designs and builds algorithmically driven features in the Datadog app. We work across a range of applications, primarily focusing on analysis on streaming data such as anomaly detection, error outliers and faulty deployment analysis. As an Applied Scientist you will work on building models and algorithms for machine learning powered features within the Datadog platform. You will work closely with our engineering and product partners to explore, build, scale and deliver these features that we incubate within the Applied AI team. At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them. What You’ll Do: Design solutions for our different use cases. You will research and benchmark relevant algorithms to find the best fit for our use-cases Leverage machine learning algorithms and statistical techniques to build new scalable product features Develop, deploy and monitor new and existing features to production Participate in our journal club by reading and presenting the latest academic research papers to the team Explore, analyze and tell the story behind high volumes of data flowing through Datadog systems Maintain and monitor the models, services and infrastructure owned by your team Participate in your team’s on-call rotation Who You Are: You have a BS/MS/PhD in a Computer Science, Engineering, Machine Learning or related scientific field or equivalent experience You have experience working with high-scale systems and datasets including building models, applying machine learning to real business problems, and writing production data pipelines You can explain complex ideas and algorithms to non-technical audiences You care about code simplicity and performa

Become a part of our caring community Most AI engineering jobs are a thin wrapper around a model API. This role is different. We build the platform that transforms millions of clinical documents into trusted, actionable data. Our systems use large language models (LLMs) to read medical records, extract structured facts, answer complex questions with citations back to the source document, and route ambiguous cases to human experts for review. Our users make decisions that impact real healthcare outcomes, so “good enough” is not good enough. Building AI systems that are accurate, reliable, auditable, and scalable is at the core of this role. As a Senior AI Applied Engineer, you will design, build, deploy, and operate production AI systems used at scale within one of the largest health insurers in the United States. You will own solutions end-to-end, from user experience and APIs to model orchestration, evaluation frameworks, infrastructure, and production operations. Why Join Us Build production AI systems where LLMs are in the critical path, not just demos or proofs of concept. Work on extraction, retrieval, agentic workflows, and human-review systems that process real healthcare data at scale. Own projects end-to-end across frontend, backend, AI orchestration, infrastructure, deployment, and operations. Solve challenging problems around accuracy, explainability, traceability, and reliability in regulated environments. Ship quickly in a small, high-impact team that embraces AI-assisted development and rigorous quality standards. Build systems that continuously improve through expert feedback, evaluations, and human-in-the-loop workflows. Key Responsibilities Design, develop, and deploy full-stack AI-powered application

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. The Credit team at Plaid builds lending solutions across the full lender lifecycle — including underwriting, verification, and servicing. We help lenders make faster, more informed decisions using consumer-permissioned financial data. Within Credit, the Insights team develops data-driven products including LendScore, Income Insights, CashFlow Insights, and Network Insights. These products power smarter credit decisioning for lenders of all sizes. As a Product Manager on the Insights team, you will drive execution across our portfolio of credit decisioning products. You’ll work closely with engineering and data science teams to ship high-quality product experiences, and partner with sales and marketing on go-to-market motions. You’ll report to the Insights product lead and play a critical role in ensuring we deliver on our roadmap commitments while maintaining the flexibility to pursue new product opportunities as they emerge. Responsibilities Own end-to-end execution of the product roadmap for LendScore, Income Insights, CashFlow Insights, Network Insights, and new Insights products Write product specs, define requirements, and work with engineering and data science to deliver features on schedule R

We’re looking for an experienced Associate to join our growing GTM Strategy & Operations team. Our team solves complex problems that require insight, creativity, and execution through building scalable processes, tools, and systems for our rapidly growing global business. You will own the day-to-day management and strategy of the sales productivity tool ecosystem that powers our Sales and Customer Success organizations - including contact intelligence, outbound sequencing, and data enrichment platforms. Doing this successfully requires a deep understanding of the “day in the life” for field teams, strong vendor management skills, and the ability to translate business needs into tooling solutions that help Datadog grow 10x. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. You Will Own the tool portfolio: Manage the strategy, lifecycle, and day-to-day administration of Datadog’s sales productivity tools, including contact intelligence, outbound sequencing, and data enrichment platforms Lead AI tooling strategy: Identify, evaluate, and pilot AI-powered tools and features that enhance sales productivity - from AI-assisted prospecting and enrichment to intelligent sequencing and personalization - and help define Datadog’s forward-looking AI tooling roadmap for the Sales and CS organizations Drive vendor performance: Manage vendor relationships, contract renewals, and commercial negotiations in partnership with Legal, Finance, and Procurement - including benchmarking tools against alternatives and driving competitive pricing Measure impact: Define and track success metrics across all managed tools (e.g., adoption rates, data quality, pipeline influenced) and use findings to make data-driven consolidation, expansion, or replacement decisions Partner with the field: Wo

This is a senior individual contributor role for someone who wants to actively shape how Engineering, one of the most important parts of how Datadog develops its people. You'll sit at the center of Datadog's biggest talent bets for Engineering: how we build leaders, define career paths and org design, evolve performance, move talent internally, and plan succession for our most critical roles. You’ll own this work end to end, from the first framing conversation with senior leaders through to delivering a program running at scale. AI is changing how Engineering builds software, and it is changing how People builds the programs that support Engineering too. This role sits at the centre of both: understanding how AI is reshaping engineering roles, skills and structures, and building AI-powered solutions within People to keep pace. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Design and lead complex talent programmes for Engineering, spanning leadership capability, career architecture, org design, performance, internal mobility and succession for critical roles. Partner directly with PBPs and senior leaders to turn ambiguous problems into clear programme goals, design principles and success measures. Help Engineering and People understand and respond to how AI is reshaping roles, skills and ways of working, and translate that shift into practical talent and org design choices. Stay hands-on from concept through to adoption: this is a build and run role, not a strategy and handover role. Work across Enablement, Learning, People Analytics and People Systems so what you build scales and embeds into core people processes. Equip PBPs with frameworks, tools and executive-ready narratives that support real adoption in the business. Operate in ambigu

About Datadog: We're on a mission to build the best platform in the world for engineers to understand and scale their systems, applications, and teams. We operate at high scale—trillions of data points per day—providing always-on alerting, metrics visualization, logs, and application tracing for tens of thousands of companies. Our engineering culture values pragmatism, honesty, and simplicity to solve hard problems the right way. The Opportunity: Datadog’s Staff Engineers are our technical leaders operating at the forefront of technology, building solutions that take us through at least our next five years of growth. They do this in three major ways: As individual contributors, they bring world class technical abilities to deliver industry leading systems in areas such as data visualization, virtual runtime profiling, and planet scale streaming. As technical leaders they bring experienced technical breadth and communication skills to tackling design and architectural problems spanning the organization, charting the right course, then leading delivery. In both roles they participate in the staff engineering community and help us learn from what the industry is doing and what we've built before, and so improve company wide standards around software and systems engineering. Some examples of projects a staff engineer may own include designing and building a new data storage engine handling hundreds of millions of records per second, being the lead engineer building a new product like synthetics or profiling, or rebuilding a critical service to handle the next two orders of magnitude of scale. What You'll Do: Be the technical owner of multiple pieces of critical architecture in your area of the business Own delivery of the systems you architect from beginning-to-end, doing what it takes to get things shipped and at full scale in production Dive deep into performance of systems; inventing new approaches that bring efficiency at scale Who You Are: You have a BS/MS/P

We're looking for a Staff Engineer to join the Logs organization at Datadog and help redefine how our customers ingest, query, and derive insights from logs data. In this role, you’ll work closely with Product Managers and customers to drive complex initiatives across ingestion pipelines, search infrastructure, and intelligent log management capabilities - all while pushing the boundaries of what’s possible with AI and distributed systems. You’ll have the opportunity to lead efforts that shape the future of log management. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Partner with Product Managers to define ambiguous product requirements and determine the most impactful solutions for customers Lead technical strategy and execution and design systems surrounding log query performance and ingestion at scale. Explore and prototype new capabilities and collaborate with peers on initiatives spanning AI-powered log management, security and business operations, advanced query capabilities, and external data sources query capabilities. Mentor engineers across levels and contribute to growing a high-performing, collaborative team culture Who You Are: You have deep experience architecting and scaling backend systems, with a strong focus on data-intensive or distributed infrastructure You excel in ambiguous environments, demonstrating a mix of drive, curiosity and pragmatic decision-making You’ve partnered effectively with Product Managers and customers to define product direction and ship impactful features You have expertise in debugging complex systems and optimizing performance across real-time data pipelines You have experience in using AI agents tools in your day-to-day engineering practices You lead by example and enjoy helping others grow through m

This is a senior individual contributor role for someone who wants to actively shape how Engineering, one of the most important parts of how Datadog develops its people. You'll sit at the center of Datadog's biggest talent bets for Engineering: how we build leaders, define career paths and org design, evolve performance, move talent internally, and plan succession for our most critical roles. You’ll own this work end to end, from the first framing conversation with senior leaders through to delivering a program running at scale. AI is changing how Engineering builds software, and it is changing how People builds the programs that support Engineering too. This role sits at the centre of both: understanding how AI is reshaping engineering roles, skills and structures, and building AI-powered solutions within People to keep pace. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Design and lead complex talent programmes for Engineering, spanning leadership capability, career architecture, org design, performance, internal mobility and succession for critical roles. Partner directly with PBPs and senior leaders to turn ambiguous problems into clear programme goals, design principles and success measures. Help Engineering and People understand and respond to how AI is reshaping roles, skills and ways of working, and translate that shift into practical talent and org design choices. Stay hands-on from concept through to adoption: this is a build and run role, not a strategy and handover role. Work across Enablement, Learning, People Analytics and People Systems so what you build scales and embeds into core people processes. Equip PBPs with frameworks, tools and executive-ready narratives that support real adoption in the business. Operate in ambiguity and influe

Datadog is seeking a Director of Product Management for Platforms to lead the internal platforms that power our global engineering organization. This is both a customer and internal platform leadership role, focused on enabling Datadog customers to maximize value with Datadog but also to enable other Datadog products to build, scale, and operate products efficiently and reliably. In this role, you will bring a combination of technical expertise, product management experience, and a deep understanding of platform and shared capabilities to help Datadog grow its leadership position. You will lead a team of product managers and collaborate with senior leadership in product, engineering and design. Your scope includes driving product features shared across all Datadog but also large-scale Datadog’s platform solutions. What You’ll Do Own the vision and strategy for platform products, ensuring alignment with overall company goals and customer needs. Identify new opportunities for innovation and drive them from concept to execution, ensuring they have a measurable impact on customers and the business. Define product roadmaps and manage the prioritization of features and initiatives to ensure the team's efforts are aligned with business goals. Drive Platform Adoption: Partner with engineering and product teams to ensure widespread adoption of shared platforms and shared features, reducing duplication and accelerating delivery. Improve Operational Efficiency: Improve developer velocity, time-to-production, and operational efficiency across Datadog’s engineering ecosystem. Collaborate with cross-functional teams including engineering, design, data science, marketing, and sales to deliver infrastructure product solutions that meet customer needs and business objectives. Define and Track Success Metrics: Define and track platform success metrics, including adoption of platform capabilities, reduction in internal toil, time-to-production improvements, cost efficienc

As a Research Scientist on our team, you will partner with Research Engineers, working on fundamental research problems and collaborating with Datadog's product and engineering teams to translate research advances into products. Building on our track record of AI-powered solutions (e.g., Bits AI , Bits Evolve , and our time series foundation model ), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security. We are focused on two research areas: World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents. Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost. What You'll Do: Conduct research in generative AI and machine learning, building specialized foundation models and trained agents for observability Train multimodal models on large-scale, diverse telemetry data (metrics, logs, traces, topology, events) using distributed training infrastructure Design and build simulated environments and RL training loops for on-policy agent training and evaluation Collaborate with cross-functional teams (Product, Engineering) to integrate capabilities like multimodal world modeling and autonomous agents into Datadog's products Stay at the forefront of foundation models, world models, and RL-based agent research Contribute to r