AI Engineering Lead

Location: Chicago, Remote

Department: Engineering

Location Type: HYBRID

Employment Type: FULL_TIME

• Build and maintain robust data pipelines at scale: Design, build, and operate the data infrastructure that powers our core product—ingesting, indexing, transforming, and moving large volumes of data reliably using powerful frameworks. You'll own pipeline orchestration, robustness, monitoring, scale, and data quality checks to make sure nothing silently breaks and stuff always works.
• Drive rigorous evaluation: Build evaluation frameworks that tell us whether our systems actually work, both data systems and AI systems. You'll define metrics that matter, run structured evaluations, and build the tooling that lets us measure real-world performance continuously—not just once at launch.
• Develop and improve ML/NLP/AI models: Train, tune, and iterate on models that power our product—working across feature engineering, model development, and deployment. You'll contribute to the full ML lifecycle, grounded in the data and evaluation infrastructure, enabling continuous quality growth.
• Ship fast and iterate: We're a startup, not a research lab. You'll make high-impact contributions with short feedback loops, balancing rigor with velocity. Expect to prototype quickly, learn from real-world performance, and continuously improve.
• Collaborate across the stack: Work closely with product and engineering to integrate data and AI capabilities into user-facing features. You'll need to translate model outputs into things users actually care about, and get hands-on with Responsiv backend code as needed.

• Have built and operated data pipelines in production: You've designed systems that move and transform large volumes of data reliably—not just happy-path demos. You understand idempotency, backfill strategies, schema evolution, and what it takes to keep pipelines healthy over time. Experience with established orchestration and processing frameworks is expected.
• Care about rigorous evaluation and dataset quality: You've designed evaluation frameworks, and got to metrics that are bug-free and evaluations that are ergonomic. You're skeptical of leaderboard scores and obsessive about understanding where things actually break.
• Have hands-on ML/AI experience: You've trained, tuned, and shipped models in production. You understand the grind of data preparation, the art of hyperparameter tuning, and why evaluation methodology matters as much as the model itself. Experience with NLP, document understanding, or classification problems is a bonus.
• Have shipped end-to-end: From data collection and pipeline construction through model training to deployment and monitoring—you've owned the full lifecycle. Experience with Azure or similar cloud platforms is a plus.
• Take pride in building robust, well-engineered systems: You enjoy the craft of turning complex data and ML systems into something that runs reliably in production. You invest in tooling, observability, and developer experience—because you know that fast debugging and smooth iteration cycles are what let you move quickly without breaking things.
• Thrive in ambiguity: You've worked in fast-paced environments where requirements shift, perfect data doesn't exist, and you have to make pragmatic tradeoffs. You take ownership, move quickly, and know when good enough is good enough—and when it isn't.
• Can bridge data, ML, and product: You're able to translate business problems into data and ML formulations and explain system behaviour to non-technical stakeholders.

• Advocate like you're right. Listen like you're wrong. We communicate ideas with both passion and humility. We are all responsible for innovation and believe that ideas can come from anyone. 
• Practice extreme ownership: With ownership comes accountability. Deliver on your commitments and take individual responsibility for the team’s successes and failures. 
• Measure success: Having clarity on what success looks like is critical to its achievement. Make sure the measurements are meaningful. · 
• Make an impact: Iterate quickly. Speed over Perfection. Results over Activity. 
• Do more with less: We value efficiency and resourcefulness. Quality is not dictated by the quantity of resources, but how effectively they are used. 
• Celebrate the wins. Acknowledge the losses. Both are important signals to help us steer the ship. Give meaning to the hard work by recognizing the moments, milestones, stories and people in our joint success.