• Build AI-Controlled Content Systems • Architect and ship AI Controllers that encode visual logic (composition, layout, constraints) into reusable systems • Translate creative direction into structured pipelines (e.g., turning logo safe zones into enforceable spatial constraints) • Design templates that produce consistent outputs across thousands of variations • Build and optimize ComfyUI pipelines integrating multiple models, refiners, and control mechanisms • Combine segmentation, depth maps, and latent constraints to guide generation toward production-grade outputs • Iterate on workflows based on output quality, failure modes, and client requirements • Own Model Behavior and Output Quality • Work hands-on with diffusion models, encoders, and latent space manipulation • Define how prompts, conditioning, and control signals interact across the pipeline • Ensure outputs meet brand and visual standards—not just 'good images,' but usable assets • Extend Platform Capabilities • Contribute to new node definitions and AI capabilities within Grip’s platform • Specify technical requirements for new features across Python/PyTorch systems • Collaborate with engineers and creative teams to push what’s possible in automated content generation

• Design and implement AI agents facilitating the full development cycle of optimization applications with a focus on leveraging Gurobi optimization expertise and best practices. • Architect and maintain the integration of optimization components, including Gurobi's solver and related libraries, into AI features of existing and new products. • Partner with cross-functional teams (AI Innovation Lab, Optimizer team, Experts, Product Management, Marketing) to align on AI feature requirements, gather domain knowledge, and ensure best practices are reflected across AI systems and products. • Develop and refine prompt and context engineering strategies for production AI systems. • Participate in the quality testing of AI features by designing test cases and evaluations. • Troubleshoot AI feature quality and performance issues. Be an escalation contact for service incidents related to AI features to help our support team when necessary. • Serve as an internal AI subject matter expert, evaluating use cases across teams and providing technical guidance and recommendations on AI applicability and approach. • Collaborate with a team of software developers and QA engineers of the Platform team following our agile methodology. The role involves a substantial amount of teamwork and individual contribution.

Location:  Need to be able to work EST timezone. Remote | Full-time Compensation: $175K - $250K We are hiring on behalf of our client who is developing a cutting-edge autonomous agent runtime focused on high-frequency financial environments. While current agents operate effectively as independent units, the next phase of evolution involves building a sophisticated intelligence layer where the entire fleet learns autonomously from real-time market outcomes. The Staff AI Engineer will be responsible for moving beyond manual propagation of insights to a system where the fleet gets smarter with every trade. This is a high-stakes production role, not a research position. The feedback loop is immediate and measurable: the work produced either enhances agent profitability or it does not. The successful candidate will own the intelligence layer that turns thousands of daily trading decisions into compounding, autonomous growth. Key Responsibilities: Learning & Optimization - Feedback Loop Implementation: Design and implement systems that connect trade outcomes back to strategy improvement, specifically focusing on signal selection, risk parameters, position sizing, and timing. - Evaluation Frameworks: Build frameworks to quantify which signals and market conditions accurately predict profitable trades versus noise. - Automated Strategy Generation: Develop systems to explore new configurations, backtest them against real fleet data, and surface candidates for deployment autonomously. - Market Adaptation: Build mechanisms to detect shifts in market conditions (e.g., trending vs. choppy) and adapt fleet behavior in real-time. Autonomous Fleet Intelligence - Fleet Monitoring: Create higher-order agents for automated monitoring to catch configuration errors and performance degradation across all concurrent agents. - Performance Attribution: Decompose trades into component drivers—signal accuracy, execution efficiency, and exit timing—to feed insights back into strategy design. - Coordination & Risk: Manage concentration risk and capital allocation across the fleet, balancing the exploration of new approaches with the exploitation of proven strategies. Model & Inference - Infrastructure Ownership: Transition from external LLM dependence to controlled intelligence, evaluating hosting strategies ranging from proxied external models to fine-tuned, domain-specific models. - Data Capture: Build the telemetry and data capture layer to ensure every decision and outcome is structured and queryable. - Domain-Specific Training: Determine the efficacy of domain-specific training over general-purpose prompting and build the necessary pipelines for implementation. - Inference Optimization: Optimize inference for many concurrent agents, ensuring structured decision outputs and cost-efficiency at scale.

Location:  Need to be able to work EST timezone. Remote | Full-time Compensation: $175K - $250K We are hiring on behalf of our client who is developing a cutting-edge autonomous agent runtime focused on high-frequency financial environments. While current agents operate effectively as independent units, the next phase of evolution involves building a sophisticated intelligence layer where the entire fleet learns autonomously from real-time market outcomes. The Staff AI Engineer will be responsible for moving beyond manual propagation of insights to a system where the fleet gets smarter with every trade. This is a high-stakes production role, not a research position. The feedback loop is immediate and measurable: the work produced either enhances agent profitability or it does not. The successful candidate will own the intelligence layer that turns thousands of daily trading decisions into compounding, autonomous growth. Key Responsibilities: Learning & Optimization - Feedback Loop Implementation: Design and implement systems that connect trade outcomes back to strategy improvement, specifically focusing on signal selection, risk parameters, position sizing, and timing. - Evaluation Frameworks: Build frameworks to quantify which signals and market conditions accurately predict profitable trades versus noise. - Automated Strategy Generation: Develop systems to explore new configurations, backtest them against real fleet data, and surface candidates for deployment autonomously. - Market Adaptation: Build mechanisms to detect shifts in market conditions (e.g., trending vs. choppy) and adapt fleet behavior in real-time. Autonomous Fleet Intelligence - Fleet Monitoring: Create higher-order agents for automated monitoring to catch configuration errors and performance degradation across all concurrent agents. - Performance Attribution: Decompose trades into component drivers—signal accuracy, execution efficiency, and exit timing—to feed insights back into strategy design. - Coordination & Risk: Manage concentration risk and capital allocation across the fleet, balancing the exploration of new approaches with the exploitation of proven strategies. Model & Inference - Infrastructure Ownership: Transition from external LLM dependence to controlled intelligence, evaluating hosting strategies ranging from proxied external models to fine-tuned, domain-specific models. - Data Capture: Build the telemetry and data capture layer to ensure every decision and outcome is structured and queryable. - Domain-Specific Training: Determine the efficacy of domain-specific training over general-purpose prompting and build the necessary pipelines for implementation. - Inference Optimization: Optimize inference for many concurrent agents, ensuring structured decision outputs and cost-efficiency at scale.