TLM, Embedded Experiences
Lead the technical direction, architecture, and execution of critical Cooperative Systems initiatives. Manage and mentor a team of engineers while maintaining meaningful hands-on technical involvement. Partner closely with stakeholders across Support, Operations, Finance, IT, Sales, Legal, and other functions to identify opportunities for AI-driven improvements. Design and build production systems that leverage large language models and other AI technologies. Drive engineering excellence through strong technical decision-making, code quality, operational rigor, and thoughtful system design. Balance rapid experimentation with long-term platform investments. Establish technical roadmaps and execution plans for projects spanning multiple teams. Coach engineers through technical challenges, career growth, and project execution. Help shape the culture, processes, and engineering practices of a growing organization.
Software Engineer, Knowledge Systems
As a Software Engineer on Knowledge Systems, you will help build systems that understand what is true about the world by extracting, connecting, retrieving, and reasoning over knowledge from the web and beyond to enable AI agents to answer questions with unprecedented precision and completeness.
Senior Product Operations Manager, Evaluation
Build and scale the systems that power model and product evaluations across Harvey; run intake, triage, and prioritization for the evaluation request queue, routing capacity to the highest-value coverage gaps; embed evaluation workflows and readiness checkpoints into the product development lifecycle; create the single source of truth for evaluation status, results, history, and launch readiness; turn Expert-designed evaluation methodologies into scalable, repeatable operational processes; manage human data providers and stand up the internal contract-attorney pipeline, ensuring evaluation quality meets legal standards; work with Engineering and Research to improve evaluation tooling, automation, and dashboards; drive evaluation readiness for major product and model launches across geographies and jurisdictions; document and operationalize evaluation governance as complexity increases; help define how Harvey ensures model accuracy, reliability, and trust at global scale.
AI Agent Engineer, Client Facing
The AI Agent Engineer will lead the building and deployment of enterprise-grade Voice, Chat AI agents and AI Copilot, owning the end-to-end lifecycle of AI Agents including building, integrating, testing, demoing to clients, deploying into production, and tuning performance. Responsibilities include implementation of AI Agents such as prompt design, workflow configuration, integrations, telephony setup, and evaluation frameworks. The role involves client engagement as the primary technical partner, leading demos, communicating progress, gathering feedback, and guiding solutions from concept to production. The engineer will configure systems integrations using APIs, handling authentication, data mapping, error handling, and integrations with CRMs, knowledge bases, and enterprise tools. Telephony integration tasks include setting up SIP/CCaaS/PSTN routing, passing metadata, configuring fallbacks, and troubleshooting call quality. The role requires prompt design and optimization, iterative testing, and performance monitoring to meet targets. The engineer acts as a strategic partner to translate customer requirements into solutions and unblock challenges in security, connectivity, and knowledge ingestion. Collaboration with product and engineering teams to escalate platform gaps and resolve technical issues while driving client implementations independently is also required.
Senior Staff Research Scientist, Speech Technologies
Design, develop, and iterate on data-driven ASR models for streaming and non-streaming conversational speech applications; research and implement state-of-the-art end-to-end speech recognition architectures tailored to the medical domain; train, evaluate, and optimize ASR models across accuracy, latency, and resource utilization dimensions; preprocess and curate large-scale speech datasets to support robust model training; collaborate closely with LLM, product, and clinical teams to integrate speech technologies into the broader Hippocratic AI platform; contribute to the team's research culture through experimentation, documentation, and knowledge sharing.
VP of Engineering
Lead the design and evolution of the AI cloud platform including GPU orchestration, compute scheduling, networking, storage, and distributed systems. Make critical decisions regarding cloud infrastructure, bare-metal deployments, and platform scalability. Participate personally in architecture reviews and key technical initiatives. Build and scale large GPU clusters supporting customer workloads and design systems for GPU provisioning, scheduling, utilization optimization, and capacity management. Drive platform reliability and performance for AI training and inference workloads, partnering closely with engineering teams on infrastructure requirements for next-generation AI systems. Remain deeply involved in engineering decisions and technical direction, contribute directly to infrastructure design and implementation efforts, review architecture proposals, system designs, and major infrastructure changes, and act as the technical escalation point for complex infrastructure challenges. Establish best practices for Kubernetes, observability, CI/CD, security, and operational excellence. Build SRE and Platform Engineering functions from the ground up. Define reliability standards including SLOs, SLIs, incident response processes, and capacity planning. Drive automation across infrastructure operations. Recruit and develop Infrastructure, Platform, and SRE teams. Build a high-performance engineering culture focused on ownership and execution. Partner with executive leadership on company strategy and infrastructure investments. Manage infrastructure budgets, vendor relationships, and capacity planning.
Operations Program Manager (Computer Vision), Public Sector
As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, while supporting end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and the resilient cloud infrastructure required for international government partners. You will take full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies. You will oversee the end-to-end health of the platform, ensuring seamless integration between the AI core and all full-stack components, from APIs to UI, to maintain a responsive and production-ready environment. You will build automated systems to monitor model performance and data drift across geographically dispersed environments, ensuring the right levels of reliability. You will manage the technical lifecycle within diverse regulatory frameworks. You will lead the response for production issues in mission-critical environments, ensuring rapid resolution and building guardrails to prevent recurrence. You will translate deep technical performance metrics into clear insights for senior international government officials. You will also partner with Engineering and ML teams to ensure lessons learned in the field influence the technical architecture and decisions of future use cases.
Forward Deployed Engineer I/II
Assist customer engagements from start to end by running discovery calls and demos, building and maintaining world class agents, participating in customer calls, and serving as the primary point of contact in a fast-paced environment. Own the full agent development life cycle including building and prototyping quickly, setting up CI/CD, monitoring live usage, iterating to targets, debugging live issues, communicating with customers, and documenting best practices to accelerate future projects. Close the feedback loop with product and platform teams by capturing unmet needs, prototyping new features, contributing directly to the codebase, and collaborating with core teams to strengthen the platform for all customers.
Research Intern, Inference (Fall 2026)
As an AI Infrastructure Engineer at Together, the responsibilities include participating in on-call rotation to respond to production incidents, building and running infrastructure using Ansible, Terraform, and Kubernetes to support scaling to a large number of concurrent users, building monitoring systems to ensure high-quality service, designing and implementing operational processes such as deployments and upgrades, debugging production issues across all services and stack levels, identifying improvements for product architecture in terms of reliability, performance, and availability, and planning the growth of Together AI's infrastructure.
Frontier Agents Intern (Fall 2026)
As an AI Infrastructure Engineer at Together AI, the responsibilities include participating in on-call rotation (Pagerduty) to respond to production incidents; building and running infrastructure with Ansible, Terraform, and Kubernetes to enable scaling for a massive number of concurrent users; building monitoring systems to ensure the highest quality service for customers; designing and implementing operational processes such as deployments and upgrades; debugging production issues across all services and levels of the stack; identifying improvements for the product architecture from reliability, performance, and availability perspectives; and planning the growth of Together AI's infrastructure.
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