Build the Future of
Enterprise AI Data

About the Role

We are hiring a founding Business Development & Sales Partner who will work directly with the CEO and CTO to drive our US market expansion. This is not a quota-carrying individual contributor role in a large sales org — this is a seat at the table. You will be one of the first commercial hires, helping define how Alchedata goes to market, acquires enterprise customers, and builds strategic partnerships.

This is a dual-function role: you will close deals as a direct seller and open doors through channel partnerships and ecosystem development. You will own the full deal lifecycle — from identifying high-value prospects to signing Statements of Work — while simultaneously building the partner infrastructure that multiplies our reach.

You are the right fit if you thrive in ambiguity, love translating technical complexity into business value, and want to be part of building something from the ground up. This role offers disproportionate impact, equity, and career trajectory for the right person.

Direct Sales Responsibilities

• Identify, qualify, and prioritize high-value enterprise prospects — specifically AI teams building LLMs, vertical agents, or specialized models in robotics and healthcare.
• Lead in-depth technical discovery sessions covering RLHF/RLAIF workflows, evaluator stacks, and model alignment pipelines to uncover genuine business need.
• Own the full sales cycle from initial demo through proposal, negotiation, and Statement of Work (SOW) signature, structuring deals for long-term expansion.
• Maintain clean CRM hygiene and deliver accurate pipeline forecasts to leadership.

Business Development Responsibilities

• Identify and engage strategic partners including AI consultancies, Systems Integrators (SIs), and complementary data platform providers.
• Experiment with reseller models and develop co-marketing initiatives with tooling providers (e.g., vector databases, MLOps platforms).
• Bring structured market intelligence back to the product and engineering teams to shape the roadmap for agentic automation.

Success Metrics

• Build a qualified pipeline of enterprise opportunities.
• Complete discovery calls with target ICP accounts.
• Establish active partnership conversations.

Qualifications

• 3+ years of full-cycle B2B sales experience selling technical products to enterprise buyers, with a track record of closing.
• Technical fluency in AI/ML concepts — you can credibly discuss the difference between pre-training and post-training, and hold your own in a conversation with an ML engineer.
• Excellent consultative communication skills with the ability to build trust, navigate complex stakeholder environments, and drive alignment across organizations.
• Startup DNA: self-directed, comfortable with ambiguity, and energized by building from scratch rather than executing a playbook.
• Willingness to travel for customer sites, conferences, and partner meetings.

Nice to Have

About the Role

Alchedata is building Data Infra 2.0 - the agent-orchestrated data platform purpose-built for next-generation AI systems. We integrate evaluation, post-training, and domain-specific RL environments into one continuous intelligence loop so frontier models can move from research demos to reliable real-world deployment.

We are looking for a highly motivated RL Environment Engineer Intern (VLM) to help design, implement, and optimize reinforcement learning environments for Vision-Language Models (VLMs) and multimodal agents. You will work on the environments that shape how models perceive, reason, act, and improve through feedback.

This is a hands-on role with real ownership. You will build high-quality, multi-modal environments used for post-training, evaluation, and data flywheel generation across our platform.

What You'll Do

• Design and implement domain-specific RL environments for VLM and multimodal agent tasks, such as visual reasoning, grounding, tool use, browser/computer interaction, document understanding, and agentic workflows.
• Build and extend multi-modal observation and action spaces spanning images, text, structured state, interface signals, and model/tool outputs.
• Design and implement reward models and reward functions for VLM and multimodal agent environments, including preference-aligned scoring, trajectory evaluation, termination conditions, validation logic, and curriculum learning pipelines.
• Integrate RL environments with our post-training stack, including SFT, preference optimization, RLHF, PPO-style training loops, and evaluation harnesses.
• Run large-scale experiments to benchmark environment quality, task difficulty, reward reliability, and generalization across model families.
• Partner closely with ML engineers and the founding team to turn research ideas into production-grade infrastructure, code, and documentation.

Requirements

• Currently pursuing a Master's or PhD degree in Computer Science, AI/ML, Robotics, Applied Math, or a related field, graduating in 2026 or later.
• Strong proficiency in Python; familiarity with PyTorch and modern ML tooling.
• Hands-on experience with at least one RL framework or environment stack such as Gymnasium, Stable-Baselines3, RLlib, CleanRL, or equivalent.
• Solid understanding of RL fundamentals, including MDPs, reward design, policy optimization, value functions, exploration, and credit assignment.
• Experience building evaluation pipelines, simulation environments, or agent systems for ML applications.
• Strong engineering instincts, including writing clean code, debugging experiments, and iterating quickly.

Nice to Have

What You'll Gain

• Ownership of production RL environments that directly shape how multimodal models are trained and evaluated.
• Exposure to the full stack: data generation, post-training, evaluation, and agent orchestration.
• The chance to work closely with a fast-moving founding team on cutting-edge VLM and agent infrastructure.
• A high-impact internship with the potential for a return offer, strong recommendation, and meaningful authorship on shipped systems.

About the Role

Alchedata is building Data Infra 2.0 — the agent-orchestrated data platform purpose-built for Physical AI. We integrate evaluation, post-training, and domain-specific RL environments into one continuous intelligence loop so the next generation of embodied AI and vision-language-action models can move from lab demos to real-world deployment at scale.

We are looking for a highly motivated Summer Intern to work directly on the design, implementation, and optimization of Reinforcement Learning (RL) environments for Physical AI and Vision-Language Models (VLMs / VLA). You will help create high-fidelity, multi-modal simulation environments that power our post-training and data flywheel. This is a hands-on role with real ownership — your environments will be used by our Nvex orchestration layer and customer models.

What You'll Do

• Design and implement domain-specific RL environments for Physical AI tasks (robot manipulation, locomotion, dexterous grasping, human-robot interaction, etc.).
• Build and extend multi-modal observation spaces (RGB + depth + tactile + proprioception + language instructions) using modern simulators such as Isaac Lab, Isaac Gym, MuJoCo, SAPIEN, or custom environments.
• Develop reward functions, termination conditions, and curriculum learning pipelines that align with real-world robot behavior and VLM/VLA objectives.
• Integrate RL environments with our post-training stack (SFT → RLHF/PPO/DPO-style training loops) and evaluation harness.
• Run large-scale experiments, benchmark environment fidelity (sim-to-real gap), and iterate rapidly based on model performance feedback.
• Collaborate closely with ML engineers and the founding team on production-grade code and documentation.

Requirements

• Currently pursuing a Master's or PhD degree in Computer Science, Robotics, AI/ML, or a related field (graduating 2026 or later).
• Strong proficiency in Python and PyTorch.
• Hands-on experience with at least one RL framework (Gymnasium, Stable-Baselines3, RLlib, or CleanRL).
• Familiarity with at least one robotics simulator (Isaac Lab/Gym, MuJoCo, PyBullet, etc.).
• Solid understanding of RL fundamentals (MDPs, policy gradients, value functions, PPO/SAC, etc.).

Nice to Have

What You'll Gain

• Ownership of production RL environments that ship to real customers.
• Exposure to the full stack: data pipeline → RL training → evaluation → agent orchestration.
• A fast-paced startup environment where your work directly impacts the next wave of Physical AI.
• Competitive summer stipend + potential for full-time offer or strong recommendation letters.