Software Engineer Core Infrastructure in New York

Become a part of our caring community Every large organization is making critical decisions today about how it will leverage AI over the next decade. Few have leaders who can both define that vision and demonstrate its viability through hands-on engineering. This role requires both. 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 to source documents, and route difficult cases to human experts. These capabilities support decisions that impact real healthcare outcomes for members. As a Principal AI Applied Engineer, you will define the technical strategy, architectural standards, and long-term vision for AI-enabled products across the organization. You will influence enterprise-wide decisions regarding AI platforms, model strategies, engineering standards, and technology investments while remaining deeply hands-on in prototyping, experimentation, architecture, and software development. This is the highest-level individual contributor role within the AI Applied Engineering organization. Success requires exceptional technical depth, organizational influence, strategic thinking, and the ability to translate emerging AI capabilities into scalable, reliable, and responsible production systems. Why Join Us Shape the long-term AI architecture and engineering direction for a large enterprise healthcare organization. Influence how AI-enabled products are designed, built, evaluated, deployed, and governed across multiple teams. Drive strategic decisions involving models, vendors, platforms, infrastructure, and shared capabilities. Prototype and validate emerging technologies before the organization invests at scale.</

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We're looking for Forward Deployed ML Engineers who want to work at the intersection of deep technical work and direct customer impact. As an ML FDE, you'll partner with leading AI companies and foundation model labs to help them achieve state-of-the-art performance on their most demanding workloads — LLM serving, model training (SFT, RLHF), audio pipelines, scientific computing, and more. You're helping teams reach outcomes most engineers can't on their own. The FDE team today includes world-class software engineers, computational scientists, ML engineers, and former founders. We're looking for people with strong engineering fundamentals, deep curiosity across the AI stack, and energy for working directly with customers on hard problems. You will: Work hands-on with companies like Suno, Lovable, Cognition, and Meta to architect and optimize production AI workloads

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We're looking for Forward Deployed Engineers on our engineering team who want to work at the intersection of deep infrastructure work and direct customer impact. As an FDE, you'll partner with leading AI companies and foundation labs on cloud architecture, networking, storage, containerization, sandboxing, and more — helping them design and ship production infrastructure on Modal's platform. The FDE team today includes world-class software engineers, computational scientists, ML engineers, and former founders. We're looking for people with strong engineering fundamentals, deep curiosity across the infrastructure stack, and energy for working directly with customers on hard problems. You will: Work hands-on with companies like Suno, Lovable, Cognition, and Meta to architect and deploy massive-scale production workloads on Modal Lead technical discovery and architect

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? Large Language Models (LLMs) continue to push the boundaries of what AI systems can do — but inference is still the bottleneck. The Model Efficiency team is responsible for pushing the limits of LLM inference efficiency across our foundation models. We explore and ship breakthroughs across the model execution stack, including: model architecture and MoE routing optimization decoding and inference-time algorithm improvements software/hardware co-design for GPU acceleration performance optimization without compromising model quality Please Note: We have offices in Toronto, Montreal, San Francisco, New York, Paris, Seoul and London. We embrace a remote-friendly environment, and as part of this approach, we strategically distribute teams based on interests, expertise, and time zones to promote collaboration and flexibility. You'll find the Model Efficiency team concentrated in the EST and PST time zones, these are our preferred locations. As a Staff Research Engineer, you will develop, prototype, and deploy techniques that materially improve how fast and efficiently our models run in production. You may be a good fit

Here at Datadog, we think about offensive security a little bit differently. We embrace automation and AI to run adversary simulations continuously across a massive cloud-native environment, and we expect our offensive engineers to build the tooling that makes that possible. We're looking for a Senior Security Engineer who can execute sophisticated red team operations, write the code that scales them, and take an AI-first approach to offensive security engineering. 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: Plan and execute red team engagements end-to-end, simulating real-world threat actors across cloud infrastructure (AWS, GCP), Kubernetes, CI/CD pipelines, and corporate environments Build and maintain custom offensive tooling, automation frameworks, and engagement infrastructure, treating offensive operations as a software engineering problem Develop custom payloads and evasion capabilities tailored to Datadog's environment and modern defensive controls (EDR, SIEM, network monitoring) Improve the efficiency of offensive operations through thoughtful use of automation and AI, accelerating reconnaissance, vulnerability analysis, and reporting workflows Partner with the Detection & Response team on purple team exercises to validate detection logic, improve alert fidelity, and influence threat models Translate offensive findings into concrete improvements by working directly with defensive security and engineering teams to close gaps Who You Are: You have 5+ years of hands-on experience in offensive security (red teaming, penetration testing, or adversary simulation) with a track record of operating against mature, well-defended environments You write production-quality code (Python, Go, or similar), can build your own tools, and automate your w

Who We Are Notion is the collaborative AI workspace where teams and agents think together . We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion. Notinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn’t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business’s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life’s work. About the Role You'll own the quality bar for Notion AI products. You’ll work with product and engineering teams to build systems to define what “good” looks like, measure our progress, and drive changes to deliver reliable and high-quality AI experiences. Your work directly shapes how Notion's AI products behave for millions of users. This isn't a traditional software engineering role. It’s an art & science role . You won't spend your days writing code. Instead, you'll focus on understanding and shaping how our AI products behave through context engineering, designing evaluation systems, and analyzing data. This team sits in our AI engineering team, working directly with engineering, product, design, and data. This role is a unique blend of ops, strategy, and product thinking. Day to day, you'll live in production data, ship prompt fixes, run evals and, in effect, shape our quality strategy. As part of that you'll shape Notion's model strategy and work directly with frontier AI labs (OpenAI, Anthropic, Google) to evaluate and launch new models. We're looking for problem-seeking generalists interested in 0 → 1 : curious people wi

The Team We are Datadog’s in-house product experts. The Technical Solutions team enables Datadog’s worldwide growth by educating potential partners and ensuring that our integration ecosystem is high-performing, secure, and valuable. Partner Technology Solutions Engineers (TSEs) are the technical bridge between Datadog and our third-party developer community. We act as consultants, helping partners build world-class monitoring solutions on the Integration Developer Platform (IDP) . The Opportunity Datadog is looking for a Partner Technology Solutions Engineer to join our fast-paced team. You will be the primary technical contact for our partners, guiding them through the entire integration lifecycle—from initial architectural design to final publication on the Datadog Marketplace. This is a unique role that combines deep technical troubleshooting with high-level consulting and platform advocacy. You will work directly with external developers and see your contributions immediately reflected in the Datadog ecosystem. You Will Act as the technical lead for partners, advising on OAuth flows, log pipelines, OpenTelemetry, and agent-based vs. API-based configurations Perform architectural assessments and deep-dive code reviews for partner integrations in the integrations-extras and marketplace repositories, ensuring they meet our Quality Rubric Solve complex technical challenges for partners via Zendesk, Slack, and dedicated technical consultations Identify friction points in our Integration Developer Platform (IDP) and partner with our internal Product and Engineering teams to build a better developer experience Maintain public-facing developer documentation and internal tracking systems ( JIRA ) to ensure transparency and scale You Are A technical expert with 3+ years of experience in a technical role (Support Engineering, Solutions Architecture, or Software Development) Proficient in at least one language (Python or Go preferred) An observability enthusiast who unders

SUMMARY STATEMENT We are looking for a Solution Architect to design the technical solutions behind our client engagements and give delivery teams a clear, workable path from concept to production. You will work across enterprise data, software applications and GenAI - translating complex business problems into practical architectures that delivery teams can build and scale. This could include architecting an agentic workflow for clinical operations, a conversational analytics product grounded in enterprise data, or an AI-enabled decision platform for commercial teams. You will work directly with clients, define the architecture, test the most important technical decisions yourself and establish the foundations for successful delivery. This is an architecture-first role with meaningful hands-on engineering: you will stay close enough to implementation to prove the architecture works and support it through production delivery, without becoming the primary engineer for every component. You will also help shape the reusable patterns, technical standards and accelerators behind Lynx’s growing AI-native life sciences practice. KEY RESPONSIBILITIES Solution Architecture Own the end-to-end solution architecture for client engagements, including data models, system design, integration patterns and technology choices. Translate business requirements into clear technical designs and implementation paths that delivery teams can build from. Design solutions spanning enterprise data, APIs, applications, cloud platforms and GenAI capabilities. Lead technical discovery with clients: understand requirements, assess existing systems and identify dependencies, constraints and delivery risks. Present architectural options and trade-offs clearly to technical teams, business stakeholders and senior leaders. Make pragmatic decisions across build speed, cost, scalability, security and maintainability. Review key implementation decisions and remain

As Engineering Manager for Threat Detection, you will lead a high-performing team that powers Datadog's detection program. Threat Detection is the organization responsible for keeping Datadog ahead of an evolving threat environment: closing coverage gaps faster, raising the bar on signal quality, and shipping detections that hold up under the scale and complexity of cloud-native infrastructure. Your team will combine direct detection expertise, platform engineering, and applied AI to ship detections at a pace and scale traditional rule-writing alone cannot match. Examples of what your team will work on include detection-authoring agents, the detection platform that powers every rule in production, coverage analysis, alert triage and response automation, and the evaluation infrastructure that holds these systems to a high bar of fidelity. Detection authorship is a shared responsibility across the organization, and your team will contribute both by building the systems that scale our authoring capacity and by writing detections directly when their domain expertise is the right tool. You will partner closely with our Security Incident & Response Team (SIRT), Cyber Threat Intelligence (CTI), AI Engineering teams, and Datadog's broader Security organization. This is a high-impact leadership role: you will grow a team of security and software engineers responsible for building and executing our detection and AI strategy. 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: Lead the strategy, roadmap, and execution of Datadog Security's shift to AI-accelerated detection and response. Drive development of high-fidelity detections as a shared responsibility across the organization, ensuring your team's systems and direct contributions raise the bar on coverage and

Datadog AI Research — Scholars Program with Carnegie Mellon University Datadog AI Research (DAIR) is partnering with Carnegie Mellon University to support a small number of PhD students working on open research problems grounded by ongoing efforts at Datadog/DAIR. You will frame a problem, run your own experiments, and write up what you find, with compute and data at a scale most academic labs cannot provide. You will collaborate with colleagues working on the same questions. The Lab And The Research: DAIR is an industrial research lab motivated by practical challenges in observability and software operation: detecting and diagnosing failures, understanding complex production environments, and helping engineers operate software more effectively. The lab focuses on creating specialized foundation models, post-training and evaluating AI agents, and building frontier-scale machine learning systems. By combining fundamental research with Datadog's large-scale, real-world data and infrastructure, the lab develops new AI capabilities and translates them into practical systems with meaningful impact. Internship projects are shaped with your DAIR mentor and your CMU faculty advisor. You do not need prior experience with observability, monitoring, or infrastructure. What You'll Do: Own a research project end to end: framing the question, running the experiments, writing it up Work directly with a DAIR mentor engaged in the same problem, and stay connected to your advisor and lab Publish, and use the work toward your dissertation See research reach production, when it works Who You Are: Currently enrolled in a PhD program at Carnegie Mellon in machine learning, computer science, statistics, or a related field Depth in at least one area relevant to the research above Comfort running real experiments — training models, working with GPUs, reading and reimplementing recent papers Evidence you can do research: conference or workshop papers, preprin

We are the Experimentation team at Datadog, a startup operating inside one of the fastest-growing companies in software. The team brings together engineers who joined through Datadog’s May 2025 acquisition of Eppo with engineers from across Datadog, and together we are building feature flagging and experimentation into the platform. Both products were recently launched this year, and we are just getting started: we are migrating customers from Eppo onto Datadog while shipping an ambitious roadmap across both flagging and experimentation. Our statistics engineers hold PhDs and are pushing the boundaries of what an experimentation platform can do. Our full-stack engineers move fast, lean on the latest AI tooling, and prototype and ship new features weekly. We talk to customers directly to shape requirements, and our roadmap is driven by our own product vision and sharpened by real customer and sales input. We are looking for an Engineering Manager to lead the team building the Experimentation App. You will own one to two squads to start, partnering closely with our engineers, statisticians, product managers, and designers to turn a deep roadmap into a shipped product. To conform to US export control regulations, candidates should be eligible for any required authorizations from the US government. What You’ll Do: Lead, mentor, and grow a team of strong full-stack and frontend engineers. Help them grow to the next level and continuously provide them opportunities to develop. Own delivery for the Experimentation App from planning through production, balancing speed with quality in a fast-paced environment. Set technical direction in partnership with your team and our statistics engineers, engaging credibly on architecture and tradeoffs. Partner with Product, Design, and customers directly to shape requirements and turn customer and sales input into roadmap. Foster a culture of fast iteration and high engineering standards through code reviews, design reviews, and blamele

As a Senior Platform Product Manager focused on AI SDLC Trusted Throughput, you will define and drive the product strategy for enabling safe, reliable software delivery at AI-native scale across Datadog’s Internal Developer Platform. As AI accelerates development velocity and system complexity, you will help evolve SDLC systems from human-supervised workflows to platforms with built-in safety, observability, and correctness guarantees. You will partner closely with engineering, security, and developer platform teams to improve deployment reliability, operational visibility, and governance while enabling both engineers and AI agents to move quickly with confidence. This role offers the opportunity to shape foundational developer infrastructure and influence how AI-powered software delivery operates across Datadog. 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: Own the product strategy, roadmap, and execution for AI-native SDLC throughput and reliability initiatives across Datadog’s Internal Developer Platform Define and drive platform outcomes aligned to DORA metrics, balancing deployment velocity with reliability, change failure reduction, and operational safety Partner with engineering, infrastructure, security, and developer experience teams to build automated validation, auditability, and risk-scoring capabilities into deployment workflows Deliver actionable SDLC observability and diagnostic capabilities that connect executive-level metrics to operational signals across the software delivery lifecycle Drive systems that monitor and validate AI-generated or AI-attributed changes to ensure correctness, compliance, and trustworthy automation Serve as a cross-functional product leader across SDLC Foundations, Security Engineering, and compl

Business Systems drives efficiency across Datadog through business process analysis, systems automation and integrations, AI agent and MCP development, and vendor/software review. The team is increasingly embedded in cross-functional initiatives across People, Finance, GTM , Legal, Recruiting, and Technical Solutions — translating ambiguous business problems into scoped, buildable solutions and owning delivery end-to-end. This is not a generalist BSA role. Each Senior BSA will own a cluster of business functions end-to-end, acting as an internal product manager for their domain rather than processing inbound requests reactively. There are two openings, each covering a different domain: People, Recruiting, Legal or Finance, GTM, Procurement As the role evolves alongside AI, Senior BSAs leverage agentic tools for data aggregation and context gathering while remaining the critical human-in-the-loop layer — owning business context and product outlook, validating use cases, managing stakeholder relationships, and making the judgment calls agents can't. 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: Discovery and scoping: Translate ambiguous asks from business stakeholders into well-defined requirements that Business Systems Engineers can build against. Many stakeholders don't know what they want or the cross-functional impact of what they're asking for — you surface both before engineering begins. Cross-functional visibility: Identify dependencies, downstream impacts, and integration considerations that requesting teams miss. Proactive opportunity identification: Develop deep domain knowledge to identify automation and AI opportunities before they become inbound requests, shifting the team from reactive in

Join VTS as an Engineering Manager. As a technical leader, you'll drive high-impact, customer-facing projects across major features in web and mobile. You’ll play a pivotal role in advancing VTS's evolution into an AI-powered platform, further cementing our industry-leading position. In this role, you will be responsible for fostering a healthy and collaborative culture, guiding the team's execution and delivery, and mentoring engineers to grow their careers and capabilities. ** Please note that this opportunity is located in New York, NY, and requires this hire to work from our office 4 days a week. ** To thrive in this role, you have: Proven experience leading software teams using agile development methodologies. A strong technical background with a proven track record of building, deploying, and maintaining scalable web applications, services, and third-party integrations. Experience working on high-impact, business-critical domains, where reliability, performance, and operational stability are essential. Demonstrated ability to recruit and develop talent, strengthening the team's capabilities through effective coaching and mentorship. The ability to engage in strategic technical discussions, adeptly manage technical tradeoffs and risks, and ensure alignment with product and business goals, especially in environments involving multiple systems and external partners. A sense of empathy for the customer and a relentless focus on shipping high-quality products. Excellent communication skills that empower you to effectively convey ideas, coach, mentor, and educate others. A keen interest in building AI-backed features and leveraging AI as an engineering productivity tool. What you'll do: Lead and Develop a High-Performing Team : Foster a healthy, collaborative, and diverse culture that reflects our company and engineering values. Coach and mentor individual team members, creating a structured environment and feedback loop that supports career development and hi

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We’re looking for strong engineers with experience building developer tools that users love to work with. Our ideal candidate is someone with a demonstrated drive to build beautiful interfaces that enhance developer productivity. Requirements: 5+ years of experience developing high-quality Python libraries with broad user-bases, ideally including some experience maintaining open-source software. Knowledge of advanced Python features, especially async programming. A strong product sense that manifests as a focus on developer ergonomics and productivity. A high level of customer empathy, good communication skills, and an openness to working directly with our users to help solve their problems. Ability to participate in on-call rotation and respond to production incidents. Ability to work in-person in our NYC or Stockholm office. Any of the following would be a plus: