Solutions Lead

Reinforced Learning; Town of Tonawanda (NY); On-site; USD 120,000 - 180,000; Full time

Job summary

Reinforced Learning in the United States (New York) seeks a technically oriented professional to bridge customer needs with running ML programs. You will own discovery, pilot design, and prototype integrations, shaping how results are measured and delivered.

You will draft proposals and technical briefs, collaborate with dataset leads and program managers, and ensure a clean handoff at kickoff to drive customer success in complex training environments.

Qualifications

• Engineering or research background with customer-facing experience.
• Familiarity with post-training evaluation workflows.
• Strong writing and whiteboard skills.
• Comfort owning a problem without a playbook.

Responsibilities

• Technical discovery with customer research teams.
• Pilot design: task sets, verification, evaluation plan, delivery format.
• Prototype integrations with customer training and evaluation stacks.
• Proposals and technical briefs.
• A clean handoff to dataset leads and program managers at kickoff.

Skills

Engineering background

Research background

Customer-facing experience

Post-training evaluation workflows

Writing skills

Whiteboard skills

Problem ownership

RL environments

Tools

RL environments

Job description

The role

Between a customer’s capability question and a running program sits a technical scoping problem. You’ll own it: understanding their training setup, proposing task distributions and formats, running pilots, and designing how results get measured.

What you’ll own

• Technical discovery with customer research teams.
• Pilot design: task sets, verification, evaluation plan, delivery format.
• Prototype integrations with customer training and evaluation stacks.
• Proposals and technical briefs.
• A clean handoff to dataset leads and program managers at kickoff.

What you bring

• An engineering or research background with customer-facing experience.
• Working knowledge of post-training and evaluation workflows.
• Strong writing and whiteboard skills.
• Comfort owning a problem without a playbook.

Helpful, not required

• Solutions engineering or forward-deployed roles.
• Hands-on experience with RL environments.