AI Engineer

• Lead the technical relationship for post-sale customers and act as their trusted advisor on deployment, adoption, and value realization
• Own account health, adoption velocity, and ongoing technical deployment and success across your portfolio
• Be an expert in all of OpenAI products across our API and agentic platform, Codex, ChatGPT Enterprise, and more and conduct technical enablement and configuration sessions across them
• Identify and validate use cases by embedding with customer teams to understand workflows and pain points
• Lead account level coordination across multiple workstreams, including new product activation, change management, and customer rollout and deployment planning
• Build strong relationships with executive sponsors and technical stakeholders and help align business goals with OpenAI capabilities
• Translate customer objectives into an actionable adoption roadmap with clear sequencing, milestones, and KPIs
• Partner with Solutions Architecture, Product, Engineering and Research by surfacing customer feedback, field patterns, and technical blockers and act as a cross functional navigator who keeps teams aligned, informed, and moving toward customer outcomes
• Guide value realization and measure impact through baselines, KPI definition, and post deployment reporting
• Facilitate workshops on use case design, adoption best practices, champion building, and internal enablement
• Help drive expansions by identifying high leverage opportunities where OpenAI’s platform can power new workflows or lines of business
• Serve as the technical advisor for existing customer implementations by guiding and optimizing account setup, configuration, etc.

AI Success Engineer – Healthcare, Life Sciences

• Define and maintain enterprise architecture standards, reference patterns, and governance guardrails for AI-enabled solutions moving from pilot to production.
• Establish decision models for build, buy, or integrate choices; model and vendor selection; data access; security; and responsible AI adoption.
• Design scalable architecture patterns for AI-enabled workflows and agentic processes across data, APIs, identity, security, and cloud platforms.
• Test new AI models, LLMs, agent frameworks, and tools to inform enterprise adoption.
• Design proof-of-concepts, define success criteria, and guide solutions from pilot to production.
• Lead architecture reviews and communicate trade-offs, risks, and growth to partners.
• Partner with teams to translate needs into governed, implementation-ready solutions.
• Ensure teams through demos, workshops, and reusable templates to support safe AI adoption.

Principal Architect – Enterprise AI

AI Research Scientist

• Ship applied AI from problem definition through deployed production
• Own one or more high-leverage initiatives per quarter with clear KPI hypotheses and delivery accountability
• Mentor junior scientists and influence technical standards within your initiative scope
• Identify high-leverage AI opportunities using business context, data diagnostics, and technical feasibility
• Design practical AI/ML solutions with clear trade-offs on accuracy, latency, cost, and reliability
• Partner closely with product, engineering, analytics, and operations to align scope, sequencing, and accountability

Senior AI Scientist

• Build agentic workflows for decision-making in healthcare
• Design and ship AI systems for healthtech
• Raise the technical bar across the organization
• Support and close exceptional founders in health technology

Principal Applied AI Engineer

Machine Learning Engineer

• Own and improve the machine learning models and systems that power the Home feed, including the Shortcuts experience.
• Design, build, and ship personalized recommendations that serve millions of Spotify listeners globally.
• Build content recommendation systems for emerging agentic and AI-powered user experiences.
• Train, fine-tune, evaluate, and optimize large language models using techniques such as supervised fine-tuning (SFT), distillation, and parameter-efficient training approaches.
• Partner closely with product managers, engineers, data scientists, and designers to define and execute experimentation strategies.
• Drive A/B testing, monitoring, model evaluation, and continuous optimization of recommendation quality, reliability, and cost efficiency.
• Improve ML platform capabilities, data pipelines, and production systems that support personalization at Spotify scale.

Senior Machine Learning Engineer, Surfaces Moments

• Performance optimization, analysis, and tuning of DL models in various domains like LLM, Multimodal and Generative AI.
• Scale performance of DL models across different architectures and types of NVIDIA accelerators.
• Contribute features and code to NVIDIA’s inference libraries, vLLM and SGLang, FlashInfer and LLM software solutions.
• Work with cross-collaborative teams across frameworks, NVIDIA libraries and inference optimization innovative solutions.

Deep Learning Software Engineer, Inference

• Define and evolve the standards, architecture patterns, and tooling choices that govern how engineers use AI across the codebase — including where to invest in codebase readiness (instrumentation, specification, evaluation) versus targeted rewrites.
• Own execution of the multi-milestone plan (readiness → validated → governed) applied to corgi and secondary repos, in partnership with squad leads — without being their manager.
• Decide what AI tooling, agent infrastructure, and evaluation pipelines the org needs, and build or oversee the build of the ones that don't exist yet.
• Set the policy and technical controls for where human-in-the-loop review is required versus where autonomous agent action (PR review, deployment) is safe — resolving the tension between adoption speed and security/quality risk.
• Write the guidance engineers actually use — AGENTS.md/CLAUDE.md conventions, review norms for AI-generated code, eval standards — and make sure it's adopted, not just published.

AI Architect

• Your mission will be to transform Neon’s unique data streams into the gold standard for AI training and evaluation.
• You will bridge the gap between our 500,000+ mobile users and the world’s leading AI labs, by directly interfacing and collaborating with your peers in these labs.
• You will define our research agenda, establishing Neon as a premier authority in audio data research and evaluation science.
• You’ll lead the development of AI assisted evaluation pipelines to characterize and validate the data our platform generates, ensuring it solves the "data bottleneck" for frontier AI models.

Senior Speech AI Researcher

• Own operations problems end-to-end — Understand the operational pain, define the solution, be its PM, architect it, build it, ship it, and iterate until it actually works in production
• Drive AI transformation across the company — introduce AI-first practices into an R&D org with legacy habits; champion new tooling and prove it with shipped results
• Design and deploy agentic systems — AI agents, human-in-the-loop workflows, operational UIs, CRM integrations, n8n/orchestration pipelines that automate ops end-to-end
• Cross boundaries freely — get into other teams' code, other domains, other stacks; no "not my area"
• Work directly with business stakeholders — translate vague operational feedback into precise, well-architected solutions
• Reports to VP, R&D; works directly with business and ops leadership — you set priorities with them, not receive them

AI Transformation Engineer

• Improve operational efficiency, resilience, and process quality by establishing scalable AI automation architecture standards and governance guardrails
• Accelerate transformation delivery and reduce rework through clear design frameworks, decision models, and reusable automation patterns
• Enhance employee and customer experience by reducing manual work, process fragmentation, and automation risk
• Embed AI-first architectural practices that improve automation scale, decision quality, and measurable business outcomes
• Lead evaluation of AI automation, agentic workflow, and transformation initiatives across the organization
• Define architectural decision frameworks and long-term platform strategy for n8n, Boomi, APIs, event-driven orchestration, and AI services to reduce duplication and increase reuse
• Establish governance guardrails for AI-enabled workflows, agents, prompts, models, data flows, and human-in-the-loop controls to ensure scalability, reliability, auditability, and responsible use
• Anticipate growth, compliance, security, and operational risks across automation platforms and transformation programs before they materialize
• Influence cross-functional roadmaps with AI Automation Engineers, BSAs, and business leaders based on architectural trade-offs, platform fit, and measurable outcomes
• Translate enterprise capability needs into clear, implementable technical designs that accelerate AI automation delivery
• Create architectural blueprints for intelligent workflows, agentic process patterns, and cross-system orchestration that reduce ambiguity and minimize rework
• Design secure, scalable, and resilient automation patterns across SaaS systems, internal platforms, APIs, data ecosystems, and AI/ML services
• Drive API-first, event-driven, and human-in-the-loop architecture standards that improve interoperability, control, and long-term maintainability
• Lead architecture reviews and represent AI automation and transformation strategy in executive and cross-functional forums

Principal Enterprise AI Architect

• AI Configuration: Design, prompt, and configure custom AI Agents to seamlessly integrate into the claims process. This includes co-pilot for adjuster guidance, Photos & Documents analysis (data extraction, summarization, automation), and designing code-less AI Agent workflows.
• Architect and Build: Proactively develop the industry’s best operational flows, utilizing Snapsheet’s Claim Platform and the most advanced AI and automation tools, developing objective metrics to show impact and ROI, and help customers adopt these new processes across their organizations.
• Client Advisement: Serve in a strategic, account management style capacity, leading weekly client meetings to understand operational pain points, desired outcomes, and roadmap priorities.
• Technical Unblocking: Proactively unblock customer bottlenecks related to AI configurations, prompting techniques, and system parameters (e.g., model settings, context window data).
• Cross-Functional Liaison: Act as the primary conduit between the client, engineering, and support teams. Translate customer needs into actionable feature requests and serve as the first line of technical support for the engineering team.
• Consultative Strategy: Make expert recommendations on system configurations and AI utilization to drive value, automation, and efficiency for the customer's claims organization.
• Continuous Improvement: Provide internal feedback on existing AI features and actively contribute to the design and implementation of new capabilities.

Senior AI Implementation Engineer

• Architect, prioritize, and execute high-volume pipeline of AI initiatives
• Translate complex marketing challenges into secure, scalable AI assets
• Proactively embed within integrated marketing teams and map operational pipelines
• Design, build, and deploy custom Gems, multi-agent orchestrations, and RAG workflows
• Enforce prompt engineering standards, data privacy, and ethical guardrails
• Identify high-potential, localized workflows and scale them into enterprise-wide assets

AI Strategy and Implementation Lead

• Partner with internal and external stakeholders to re-envision and scale Core Engineering technologies, including the design and build of a suite of AI Agents
• Drive technical excellence and governance of AI Agents as use-cases are identified across Snap Engineering
• Collaborate across teams to understand product requirements, evaluate architectural trade-offs, and deliver solutions needed to build innovative products and services
• Champion best practices in availability, scalability, operational excellence, and cost management
• Serve as a technical mentor to senior engineers and a thought partner to engineering leaders
• Contribute to the technical roadmap for AI Agents and Core SWE infrastructure, ensuring alignment with organizational goals
• Partner closely with Product, Finance, Sales, and Engineering teams distributed across multiple offices
• Influence key architectural and implementation decisions for scalable, reliable, and cost-effective engineering solutions
• Iterate quickly without compromising quality and continuously raise the bar on operational excellence
• Adapt technical plans nimbly in response to evolving customer and business needs

Staff Software Engineer, DevProd AI

• Partner with business and technology teams to identify, evaluate, and prioritize AI use cases.
• Translate business needs into scalable AI solution designs and implementation plans.
• Lead delivery of AI-enabled products and solutions from concept through production.
• Provide technical leadership to engineers, contractors, and delivery teams.
• Guide architecture, design, and implementation decisions related to AI and software engineering.
• Present AI capabilities, demonstrations, and solution recommendations to business and technical stakeholders.
• Collaborate with enterprise AI, architecture, and technology organizations to align initiatives and leverage existing capabilities.
• Influence AI strategy, adoption, and best practices across WCIB.
• Support team growth through mentoring, leadership, and participation in hiring activities.

AI/ML Enablement Lead Engineer

LLM Engineer

• Design and build GenAI-powered developer tooling: prompts, IDE integrations, Cursor/Claude rules, and telemetry to understand how they’re used.
• Build and run the GenAI developer platform: Tools, services, and integrations leveraging LiteLLM to power both local and background/long-running agents, providing auth, configuration, and LLM routing.
• Own guardrails and observability for agentic workflows: integrate with Datadog, DX, and FinOut, define sensible standards, and make sure agents interact with Jira, GitHub, and internal platforms in a safe, auditable way.
• Work closely with engineers and managers across Product Engineering to understand how they build software today and to design solutions that actually fit the way they work.
• Support our mission and values, including journalistic independence, and keep ethics and safety central to how we use AI in developer workflows.
• This role includes limited on-call responsibilities; the schedule will be set when you join. Demonstrate support and understanding of our value of journalistic independence and a strong commitment to our mission to seek the truth and help people understand the world.
• You will report to the Engineering Manager of the DevEx Platforms team

Senior Engineer, Developer Platforms, Generative AI

• Own Mercury's knowledge infrastructure
• Build the knowledge layer on top of Mercury's AI infrastructure
• Own the reporting layer
• Drive company-wide adoption of standardized systems and practices
• Continuously improve how Mercury captures, organizes, and uses knowledge

AI Context Operations Lead

• run discovery with customer stakeholders across Procurement, Finance, Legal, and IT, and translate their process into an Omnea configuration
• design the target workflow: approval chains, routing logic, integrations, and the edge cases that make procurement hard
• define success criteria and a realistic path to go-live with the customer's project sponsor
• direct Omnea's AI-assisted deployment tooling to build first-draft workflows, configurations, and integration mappings from your specifications
• write specifications precise enough for AI to execute correctly. This is the core skill of the role, not an afterthought
• validate every AI-generated output against the customer's actual process, policy, and edge cases before anything ships
• handle what AI can't: judgment calls on ambiguous requirements, custom logic, and anything that needs a human read on internal politics
• present configured workflows to stakeholders, from procurement analysts to CFOs, and win their buy-in
• project-manage the deployment: timelines, blockers, stakeholder alignment, and a clear path to launch, acting as an extension of the customer's team
• track adoption after go-live and keep the configuration working as the customer's process evolves
• feed what you learn back to Product and Engineering. You'll help prioritise the roadmap and beta-test new modules as they ship

AI Solutions Lead

• Take Asset Hub’s unique real-world assets — production codebases, workspaces, and databases — and identify how each can become a high-value AI-training product: RL environments, agentic task suites, evals and verifiers, benchmarks, and fine-tuning or trajectory datasets.
• Design and build the pipeline that turns a raw asset into a derivative work: repository ingestion, test harnessing, commit-mining for task extraction, Docker/sandbox reproducibility, verifier and reward scripts, and QA tooling.
• Wrap real data in interactive environments — sandboxed application state, MCP servers, and browser/Playwright layers — that buyers can train and evaluate agents against.
• Spot the commercial opportunity in the inventory: which assets map to current lab and RLE demand, and what derivative product maximizes their value.
• Prototype quickly, then harden the best ideas into repeatable, scalable pipelines so derivative-work creation isn’t one-off.
• Partner with the Asset Hub buyer/BD side and directly with technical stakeholders at labs and RLE buyers to shape what we build to their training needs.
• Work with sensitive material — codebases, workspace exports, and proprietary datasets — with strong attention to security, privacy, licensing, and PII handling.
• Write clean, well-tested code and use AI tooling to move faster; collaborate closely with product, engineering, and the GM of Asset Hub.

AI/ML Engineer, RL Environments – Asset Hub

• Drive measurable business impact across portfolio companies through AI-powered automation
• Evaluate and select third-party AI platforms
• Lead end-to-end implementation and configure workflows
• Define baselines and success metrics before every deployment
• Design and build targeted AI automation solutions

Machine Learning Engineer jobs in New York

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AI Success Engineer – Healthcare, Life Sciences

Principal Architect – Enterprise AI

Senior AI Scientist

Principal Applied AI Engineer

Senior Machine Learning Engineer, Surfaces Moments

Deep Learning Software Engineer, Inference

AI Architect

Senior Speech AI Researcher

AI Transformation Engineer

Principal Enterprise AI Architect

Senior AI Implementation Engineer

AI Strategy and Implementation Lead

Staff Software Engineer, DevProd AI

AI/ML Enablement Lead Engineer

Staff Software Engineer, DevProd AI

Senior Engineer, Developer Platforms, Generative AI

AI Context Operations Lead

AI Solutions Lead

AI/ML Engineer, RL Environments – Asset Hub

Forward-Deployed AI Engineer