Senior Engineering Manager, Reinforcement Learning Environments (RLE)
The Senior Engineering Manager for the Reinforcement Learning Environments (RLE) team leads and grows a high-performing team of 8-9 engineers building reinforcement learning environments. This role involves managing, mentoring, and developing senior engineers and future engineering leaders. The manager partners closely with research, product, and operations teams to define the roadmap and execution priorities, drives the technical architecture for scalable, reliable, and extensible environment systems, and builds plug-and-play environments that integrate seamlessly with model training pipelines. The role balances platform rigor with operational complexity and data quality requirements, establishes engineering best practices around reliability, observability, and performance, and fosters a culture of ownership, velocity, and high technical standards.
Senior Manager
Lead transformational AI system implementations by scoping high-value solutions and navigating complex technical challenges alongside technical colleagues. Manage enterprise life sciences accounts, including oversight of pricing, contract negotiations, resourcing, and identifying strategic growth opportunities. Build deep trust with senior stakeholders in global enterprises through understanding how Frontier addresses their operational problems. Advocate for customer needs internally by providing product development teams with direct insights to refine and enhance the platform. Create scalable delivery assets such as playbooks and process improvements to empower external partners and internal teams. Collaborate across functions including engineering, data science, and business development to explore novel use cases and ensure seamless project coordination.
AI Implementations Manager
The AI Implementation Manager is responsible for the end-to-end delivery and stabilization of Ema's agentic AI solutions, spanning from design alignment through production rollout and steady state. This role involves ensuring solutions align with Ema’s agentic architecture and platform capabilities. The manager must develop a deep understanding of customer business processes and constraints to translate business workflows into feasible agentic AI workflows. They provide delivery-focused technical oversight, anticipating potential implementation issues such as integration, data quality, scale, and edge cases. The manager serves as the primary delivery contact for customer business and IT stakeholders and coordinates across multiple internal teams including Engineering, Product, Data, Infrastructure, and Value Engineering. They manage delivery under pressure by coaching stakeholders and teams during high-stress phases to reduce chaos. They communicate delivery progress, risks, and decisions clearly to all audiences, tracking success through adoption signals and outcome-adjacent metrics. Additionally, the role includes providing day-to-day delivery leadership and mentorship, promoting shared standards, clear ownership, and delivery discipline.
Technical Program Manager, Quality
Manage the end-to-end lifecycle of LLM projects, navigating the transition from research milestones to production-level deployments. Transform subjective user feedback into objective metrics and datasets. Design and implement technical evaluations to address issues found in the field and help integrate these evaluations into existing pipelines. Track internal and external feedback to ensure identified issues are followed through to resolution in subsequent iterations. Maintain the technical roadmap for voice-based capabilities, proactively identifying dependencies and resolving technical blockers across teams. Ensure the roadmap incorporates the work and constraints of all teams to deliver a cohesive user experience.
AI Deployment Manager
As an AI Deployment Manager, you will lead end-to-end AI deployments from kickoff to successful launch, owning project planning, timelines, execution, and delivery across customer implementations. You will act as a trusted partner to customers, helping translate business goals into successful AI deployments. You will deploy and operationalize AI models across Cresta's platform in partnership with internal teams, including rules-based models, summarization, generative knowledge assistance, and more. You will drive value realization, ensuring deployments deliver measurable results rather than just go-live dates. You will guide customers confidently through every phase of deployment, keeping momentum high and stakeholders aligned. You will collaborate closely with Solutions Engineering, Product, Customer Success, and Engineering teams. Additionally, you will anticipate risks, solve problems, and keep complex initiatives moving forward.
Manager, Forward Deployed Engineering
Lead and grow a team of Forward Deployed Engineers (FDE) delivering production systems with frontier models. Own end-to-end delivery outcomes through clarity, speed, tight coordination, and technical quality. Codify successful practices into tools, playbooks, and roadmap inputs to create leverage for OpenAI and the wider developer community. Identify early indicators in product behavior, customer environments, or delivery practices and raise them with urgency. Use judgment to distinguish which issues require action. Set a high performance bar for FDEs and support each person's growth through direct, actionable feedback. Define staffing and support models for field teams that can scale without added complexity.
Infrastructure Engineer
Help users discover and master the Dataiku platform through user training, office hours, demos, and ongoing consultative support. Analyse and investigate various kinds of data and machine learning applications across industries and use cases. Provide strategic input to the customer and account teams that help our customers achieve success. Scope and co-develop production-level data science projects with our customers. Mentor and help educate data scientists and other customer team members to aid in career development and growth.
Manager, Delivery Excellence
The AI Outcomes Manager partners with executive sponsors and end users to identify high-impact use cases and convert them into measurable business outcomes using Glean. They lead strategic reviews and advise customers on their AI roadmap to maximize platform value. Responsibilities include translating business needs into problem statements, success metrics, and practical AI solutions, collaborating with Product and R&D to influence priorities. They conduct discovery workshops, scope pilots, guide rollouts, and drive broad adoption of the Glean platform. The manager designs and builds AI agents for customers, redesigning business processes for effectiveness and usability, proactively identifying opportunities for expansion and engagement across teams and functions.
Senior Product Operations Manager, Evaluation
Build and scale the systems that power model and product evaluations across Harvey. 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 relationships with human data vendors and ensure 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.
Strategist, Agent Development
As a member of the Agent Strategist function at Sierra, the role involves partnering with Agent Product Managers and Agent Engineers to scope, build, and ship AI agents that manage thousands of customer conversations daily. Responsibilities include building, designing, and refining conversational AI agents, driving execution and delivery of complex, high-visibility agent development projects, and coordinating across technical and non-technical stakeholders throughout the full agent development lifecycle. The role requires ensuring clear communication among all stakeholders, developing strong relationships, and contributing data-driven, strategic insights to customers and internal team decisions. Additionally, the strategist acts as a trusted advisor to customers, helping to drive their AI strategies.
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