Lead Software Engineer (Machine Learning)
Set the technical direction and oversee delivery of high-risk, ill-defined software and infrastructure projects, balancing strategic trade-offs and helping teams prioritize in shifting environments, taking full ownership of successful outcomes for challenging projects. Design and develop reliable, production-grade machine learning systems and justify critical architectural decisions to ensure robust delivery. Develop clear, comprehensively scoped roadmaps for novel solutions to help customers achieve strategic goals and accurately estimate effort on large workstreams for timely delivery. Engage with technical and non-technical customers at all stages of the customer lifecycle, providing reasoned and credible advice and opinions on a broad range of engineering topics. Collaborate proactively within multidisciplinary delivery teams and across the engineering community to overcome technical challenges. Coach team members on specific technologies and drive the development of shared organisational resources and libraries to streamline delivery and improve engineering methods across the company. Lead the hiring and selection process and mentor multiple individuals and managers to define the future shape of the engineering team.
Speech Software Engineer
Lead the design and implementation of a scalable, high-availability voice infrastructure that replaces legacy systems. Build and refine multi-threaded server frameworks capable of handling thousands of concurrent, real-time audio streams with minimal jitter and latency. Deploy robust ASR > LLM > TTS pipelines that process thousands of calls concurrently. Develop robust logic for handling media streams, ensuring seamless audio data flow between clients and machine learning models. Build advanced monitoring and load-testing tools specifically designed to simulate high-concurrency voice traffic. Partner with Speech Scientists and Research Engineers to integrate state-of-the-art models into a production-ready environment.
Senior Staff Systems Engineer
Drive the architectural vision for the GenerativeAgent product by designing and building a highly scalable, multi-agent platform for real-time voice and text customer service experiences across various industries. Act as a technical authority and advisor for multiple engineering teams, develop system design and technical roadmaps, and define communication, state management, and orchestration patterns for multi-agent systems. Design and implement scalable, multi-tenant deployment architectures, own and define system-level SLOs/SLIs focusing on latency, cost-efficiency, and fault tolerance, identify systemic risks with proactive mitigation strategies, partner with Security and Compliance teams to meet regulatory and security requirements, lead post-incident analysis and improvements, and collaborate cross-functionally with Product, Customer Engineering, Site Reliability Engineering, TPMs, and Research to translate business requirements into system designs and productionize ML research. Mentor senior engineers and communicate complex technical concepts to both technical and non-technical stakeholders.
Site Reliability Engineer, Inference Infrastructure
As a Site Reliability Engineer on the Model Serving team, you will build self-service systems that automate managing, deploying, and operating services, including custom Kubernetes operators supporting language model deployments. You will automate environment observability and resilience, enabling all developers to troubleshoot and resolve problems, and take steps to ensure defined SLOs are met, including participating in an on-call rotation. Additionally, you will build strong relationships with internal developers and influence the Infrastructure team’s roadmap based on their feedback, as well as develop the team through knowledge sharing and an active review process.
Staff Software Engineer, Inference Infrastructure
The role involves building high-performance, scalable, and reliable machine learning systems, specifically working on the Model Serving team to develop, deploy, and operate the AI platform that delivers large language models through API endpoints. Responsibilities include working closely with multiple teams to deploy optimized NLP models to production environments characterized by low latency, high throughput, and high availability. The role also includes interfacing with customers and creating customized deployments to meet their specific needs.
Solutions Engineer (AI/ML, Pre-Sales)
The Solutions Engineer (AI/ML, Pre-Sales) will work closely with strategic customers to understand their data curation needs, business challenges, and technical requirements. The role involves leading end-to-end customer proofs of concept (PoCs) that connect data curation to training behavior and evaluation outcomes, including dataset analysis, training plan design, and interpreting results. They will partner with customer machine learning teams to map data and curation strategies, design and execute evaluation plans for base and post-trained models, select appropriate benchmarks and metrics, and run model evaluations. Additionally, the engineer will produce customer-ready evaluation reports detailing methodology, metrics, baselines, ablations (e.g., curated vs raw data), conclusions, and recommendations for productionization. They must communicate technical results effectively to both ML experts and executive stakeholders, explaining tradeoffs in compute, latency, and deployment cost. Collaboration with go-to-market, engineering, and research teams is essential to deliver compelling demos, align on requirements, and incorporate customer insights into model training and product strategies. The role also includes providing technical guidance, training, and documentation to enable prospects to confidently assess the solution.
Product Security Applied AI Intern, Summer 2026
Assist in designing and implementing custom large language models (LLMs) and fine-tuning models for specific tasks. Build and experiment with agent libraries and workflow orchestration frameworks. Explore neo-cloud technologies, containerized environments, and virtualized infrastructure. Learn and apply security and privacy best practices in AI pipelines and deployments. Collaborate with the team to document, test, and optimize agent behaviors and models. Participate in knowledge sharing and mentorship sessions to gain exposure to AI, cloud, and security tradecraft.
Customer Success Solution Architect (Brazil)
The Solution Architect is responsible for developing detailed and scalable architectural designs to address client needs using Articul8 products and third-party libraries and tools. They run pilot programs with customers to demonstrate the feasibility and advantages of proposed solutions, including introducing new product features or building prototypes. The role requires working closely with clients to understand business challenges and technical requirements through workshops, meetings, and presentations. They optimize solutions for performance, reliability, and cost-effectiveness, selecting appropriate instance types, auto-scaling configurations, and storage options. Ensuring solutions comply with security best practices and regulatory requirements is necessary, including implementing identity and access management, data encryption, and other security measures. The architect also creates comprehensive documentation and provides training on solution implementation and management. Collaboration with cross-functional teams such as Applied Research, Engineering, Quality Assurance, and Customer Success is required to incorporate innovation and maintain product leadership. Additionally, the role involves mentoring and guiding junior team members and helping to build a culture of rapid innovation.
Senior Solution Architect - Customer Success (USA)
The Senior Solution Architect will guide customers through their entire AI journey from initial solution architecture and technical discovery in pre-sales to hands-on implementation and optimization post-sale. Responsibilities include deeply understanding customer business challenges and crafting AI prototypes on the Articul8 platform to address business objectives, leading technical workshops, hackathons, and training sessions to enable customers, collaborating with Sales, Product, and Engineering teams to position the platform and deliver solutions, overseeing installation, configuration, and scaling of the platform in customer environments with a focus on security, reliability, and performance, developing and implementing tailored workflow solutions, architecting and tuning Kubernetes-based environments on AWS, Azure, GCP, and on-premises, delivering enablement workshops and documentation for long-term customer autonomy, monitoring and refining deployments for cost-effectiveness, scalability, and resilience, and gathering customer feedback to influence product roadmap and enhancements.
Staff/Senior AI/ML Engineer - (Dublin, CA)
Design, develop, and deploy AI/ML models ranging from traditional ML regression algorithms to transformer-based architectures. Train, fine-tune, and optimize deep learning and LLM-based solutions. Engage with customers to understand their needs and transform them into actionable tasks for developing new functionalities within the Articul8 platform. Collaborate with researchers, software engineers, and product teams to integrate new AI capabilities into applications. Implement and evaluate state-of-the-art automated testing and metrics to improve model accuracy and efficiency. Optimize models for both cloud and on-premises environments to ensure low latency and high availability. Develop APIs and micro-services to serve AI models in production. Stay current with the latest AI models, research, and best practices. Ensure ethical AI practices, data privacy, and security compliance.
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