Head of RL Delivery | Frontier AI Data

Morpheus Talent Solutions; San Francisco (CA); Sur place; USD 180 000 - 260 000; Plein temps

Résumé du poste

Morpheus Talent Solutions seeks a Deployment Lead to own end-to-end delivery of complex RL-centered customer programs, from scoping through steady-state execution. You will translate RL requirements into execution plans, coordinate multi-team efforts, and own deployment economics.

You will partner with senior leadership to manage timelines, risks, and margins, interfacing with RL environments, reward models and evaluation data to ensure successful deployment at scale.

Qualifications

• Significant experience owning complex, cross-functional programs in a customer-facing environment.
• Working familiarity with reinforcement learning, RL environments, reward modeling or post-training pipelines.
• Strong financial numeracy with margin management and funding decisions.
• Ability to rapidly understand complex RL systems and requirements.
• Experience communicating technical and economic tradeoffs to non-technical stakeholders.

Responsabilités

• Own end-to-end delivery of RL projects for major customers from scoping to launch and ongoing execution, accountability for throughput, quality, delivery and margin.
• Own the economics of deployments: monitor margin, manage cost-to-revenue, and decide on staffing and project structure.
• Serve as the customer's main point of contact for RL environments, reward models, and evaluation datasets, including timelines and risks.
• Develop detailed understanding of customer RL requirements: environment design, reward specs, task construction, system constraints, data flows.
• Translate requirements into clear project plans, milestones, owners, success metrics and a margin model.
• Advance projects through gates like environment calibration and validation to ensure quality and margin across the portfolio.
• Coordinate TPMs and forward-deployed engineers across concurrent RL projects, tracking progress, risk and dependencies.
• Communicate tradeoffs and risks clearly to stakeholders and internal teams.
• Identify risks early and implement mitigation plans; escalate when timelines or margins are at risk.
• Establish operating cadences, dashboards and playbooks as the organization scales RL deployments.

Connaissances

Cross-functional program mgmt

RL knowledge

Financial margin ownership

Technical fluency RL systems

Communication with ML teams

Partnership with engineers/PMs

Delivery leadership

Risk and escalation handling

Description du poste

Full-time

Morpheus are partnered with a leading frontier AI data company offering an integrated platform, a frontier data and RL environment service powered by subject matter experts, and an expert marketplace connecting AI teams with skilled annotators and RL environment builders. They have pioneered data-centric approaches that now underpin reinforcement learning for frontier models. As labs shift research investment toward RL, environments and reward modeling, this work is becoming central to how AI capabilities advance.

The role

The Deployment Lead owns the successful delivery of complex, RL-centered customer programs from initial scoping through steady-state execution: on time, to the quality bar and at margin. This is a senior, customer-facing role for operators who can translate RL and evaluation requirements into clear execution plans, coordinate multiple internal workstreams building environments and reward signals, and own the economics of the deployment.

You will sit at the intersection of customer relationship management, technical program management for RL pipelines, and operational and financial execution. You will partner with senior leadership, who own the broader customer relationship and growth, while personally owning delivery and deployment economics for RL data, environments and evaluation programs.

What you’ll do

• Own end-to-end delivery of RL projects for one or more major customers, from scoping and technical design through launch and ongoing execution, accountable for throughput, quality, delivery and margin
• Own the economics of your deployments: instrument margin live, manage the cost-to-revenue picture across the account, and make in-flight calls on comp structure, staffing and project structure
• Act as the customer’s primary point of contact for delivery of RL environments, reward models and evaluation datasets, including timelines, technical requirements, risks and operational performance
• Develop a detailed understanding of each customer’s RL requirements: environment design, reward specification, agentic task construction, system constraints, integrations, data flows and acceptance criteria
• Translate those requirements into clear project plans, milestones, owners, success metrics and a margin model
• Move account projects through their gates (environment calibration, reward-model validation, staged ramp, delivery acceptance) so quality and margin hold across the portfolio
• Coordinate TPMs and forward-deployed engineers across multiple concurrent RL projects, maintaining a clear view of progress, staffing, risk, margin and dependencies
• Communicate technical and economic tradeoffs and implementation risks clearly to customer stakeholders and internal teams
• Identify risks early, put mitigation plans in place, and elevate when timelines, feasibility, quality or margin are at risk
• Establish operating cadences, dashboards and escalation paths, and improve RL deployment playbooks as the organisation scales

What you bring

• Significant experience owning complex, cross-functional programs in a customer-facing environment, as an operator who has run things rather than advised on them
• Working familiarity with reinforcement learning, RL environments, reward modeling, agentic evaluation or post-training pipelines, and the ability to get fluent fast where gaps exist
• Financial and commercial numeracy: comfort owning margin, building and reading the unit economics of a program, and making the call to protect it
• Strong technical fluency and the ability to quickly understand complex RL systems, environments and implementation requirements
• The ability to communicate credibly with research and applied ML teams, while translating technical detail into clear decisions for non-technical audiences
• A track record of partnering effectively with engineers, program managers and cross-functional operators
• Excellent judgment in distinguishing technical, execution and customer-management issues, and finding the right path to resolution
• A strong record of delivering against ambitious timelines while holding a high bar for quality
• Comfort in ambiguous environments where requirements evolve and processes are still being built

Nice to have

• Direct experience deploying or scaling RL environments, reward models or agentic evaluation suites for frontier labs
• A background in consulting, banking, private equity or VC, or an operating role where you owned a P&L or a hard number
• Experience deploying technical products, services or data-intensive programs
• Experience working directly with forward-deployed engineers, research engineers, solutions engineers or implementation teams
• Experience managing strategic accounts with multiple parallel workstreams
• Experience building repeatable operating systems for a rapidly scaling organisation

What success looks like

In your first several months, you will take ownership of a major customer’s RL deployment, develop a strong understanding of its environment, reward and evaluation requirements, stand up a clear operating cadence, and bring real visibility to delivery risk and margin. Over time, you will help define how the organisation deploys complex RL programs at scale.