AI Engineer at Responsiv – Chicago, Illinois

Responsiv Overview Responsiv is at the forefront of the legal AI revolution. The legal industry—historically slow to embrace technological change—stands on the cusp of unprecedented transformation, and we're leading the charge. As AI reshapes how legal work gets done, we're building the tools that will define this new era.
Backed by Greylock and OnDean Forward (Andrew Sieja, founder of Relativity).
What we doResponsiv helps organizations stay ahead of regulatory change. Laws and regulations are constantly evolving—and for businesses in regulated industries, keeping up isn't optional. We build AI that understands how the regulatory landscape is shifting and maps those changes to what businesses actually need to do about it. Our platform monitors regulatory updates, identifies what matters to each organization, and surfaces the gaps between new requirements and existing compliance programs. Role Responsibilities

• Build and improve AI/ML/agentic systems end-to-end: design, train, evaluate, and deploy models that power our core product—from data pipelines and feature engineering through model development, evaluation, and production deployment. You'll work across the full ML lifecycle, not just notebooks.
• 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, build representative datasets, learn from real-world performance, and continuously improve.
• Production AI infrastructure: Deploy and manage AI and agentic workloads in Azure. Build reliable, low-latency systems that serve predictions in production. Own the infrastructure that keeps models running smoothly—monitoring, versioning, and retraining pipelines included.
• Collaborate across the stack: Work closely with product and engineering to integrate 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.

You're a good fit if you..

• Have deep hands-on AI/ML experience: You've trained, tuned, and shipped models in production—not just run tutorials. You understand the grind of curating high-quality datasets, the art of hyperparameter tuning, and why evaluation methodology matters as much as the model itself.
• Care about rigorous evaluation: You've designed evaluation frameworks, know the difference between metrics that look good and metrics that matter, and have debugged models that worked in notebooks but failed in prod. You're skeptical of leaderboard scores and obsessive about understanding where your models actually break.
• Have shipped ML systems end-to-end: From data collection and labeling through training pipelines to deployment and monitoring—you've owned the full lifecycle. Experience with Azure ML or similar cloud ML platforms is a plus.
• Take pride in, and enjoy building robust, well-engineered systems: You enjoy the craft of turning complex 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 ML and product: You're able to translate business problems into ML formulations and explain model behavior to non-technical stakeholders. Bonus if you've worked in NLP, document understanding, or classification problems.

At Responsiv, our core values are not just words on a page; they are the guiding principles that shape our culture, define our actions, and propel us towards our vision. ·

• 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.