Note: The job is a remote job and is open to candidates in USA. Responsiv is building AI tools to help organizations understand regulatory change and identify gaps in their compliance programs. The AI Engineering Lead will build and maintain scalable data pipelines, develop rigorous evaluation frameworks, improve ML/NLP/AI models, and integrate data and AI capabilities into user-facing products.
Responsibilities
- 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
Skills
- 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
- Have shipped end-to-end: From data collection and pipeline construction through model training to deployment and monitoring—you've owned the full lifecycle
- 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
- Experience with NLP, document understanding, or classification problems is a bonus
- Experience with Azure or similar cloud platforms is a plus
Company Overview