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, Agents & Automations
Design, build, ship, and maintain core capabilities for North’s Agents & Automations platform. Build product and platform features that help users create, run, debug, evaluate, and improve agents and automations. Own features end-to-end, from technical design through implementation, testing, launch, and iteration. Work across the stack, from frontend product surfaces to backend systems, depending on what the product needs. Make practical technical decisions that balance speed, quality, depth, and user impact. Collaborate closely with product, design, modelling, customer-facing teams, and other engineers to define the right outcomes and ship measurable improvements. Use AI actively in your work, while staying intellectually engaged and accountable for the quality and reliability of what you ship.
Senior Product Manager - Machine Learning (m/f/d)
Own the machine learning product roadmap for core speech and NLP across markets, turning field signals into clear priorities. Run a structured customer-feedback to ML loop with Sales, Customer Support, and Customer Success, including direct customer conversations and closing the loop back to the field. Define and maintain the model evaluation framework that gates releases, including metrics, slices, thresholds, and regression bars. Partner closely with Speech & NLP Leads to ship improvements to production. Improve product analytics and ML data integration by closing gaps between goals, production measurements, and model training data. Own ML product KPIs such as no-edit rate, documentation rate, edit rate per slot, and transcription accuracy, and drive measurable improvements.
Software Engineer
Develop low-level drivers for the sensors or actuators of robots, develop the OS and middleware of the robots, integrate embedded algorithms such as Guidance, Navigation, Control, and Computer Vision, optimize runtime of algorithms on various hardware accelerators like GPU, TPU, DSp, develop the backbone of a command and control system for massive data ingestion and processing, develop a web-based front-end to display theatre of operations and allow mission conduction, build internal tools to improve efficiency and reduce technical debt, develop connectors between existing company tools like ERP, MES, PLM, implement code into production-ready environments, ensure seamless integration with Harmattan AI’s systems, conduct rigorous code reviews, test algorithms in real-world environments, develop monitoring tools, track model performance and continuously improve deployed solutions, collaborate closely with other hardware and software teams to align development with system requirements, and communicate findings effectively to stakeholders.
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.
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