Senior/Lead Machine Learning Engineer

• We prioritize customer needs first
• We work in small, project-based teams
• We have flexibility in terms of the problems we work on
• We own the full lifecycle of our projects
• We avoid silos and encourage taking up tasks in new areas
• We balance quality and velocity
• We have a shared responsibility for our production code
• We each set our own routine to maximize our productivity
• You’ll join us as a Machine Learning Engineer/ Data Scientist for Mimica’s Data Intelligence team, whose goal is to transform raw recorded data into clean, structured, anonymized data that powers the work of other teams.
• Your work will be applied and LLM-heavy, with opportunities to evaluate and fine-tune open-source models and build smaller, task-specific models.
• If you enjoy taking ML beyond experiments, this is your chance to shape the systems at the heart of Mimica’s products and see your work deliver value to real users at scale.
• Own, debug, and improve production pipelines for accuracy and efficiency.
• Design, build, and iterate on LLM evals and LLM-as-a-judge systems, including the datasets, metrics, and benchmarks to monitor quality.
• Run rigorous experiments on prompts, pipeline design, model choice, and inputs (e.g., OCR text vs. image + OCR), and turn the results into clear recommendations.
• Drive LLM efficiency improvements across our pipelines by comparing models, restructuring steps, and replacing LLM calls with smaller, purpose-built models where it makes sense.
• Expand our anonymization pipeline, including evaluating and adapting existing models.
• Work with Python and quality engineers to take experiments into production, and share responsibility for the code you ship.
• Document findings in writing (eg. experiment reports, design docs)
• Primarily, we’re looking for an individual with an experiment- and data-driven researcher mindset with good coding habits. This includes:
• Solid machine learning/data science background: you understand experimental design, data splits, metrics, bias, and statistical significance, and you know when a result is real.
• Hands-on experience applying LLMs in production or near-production settings, especially evaluating them: building eval sets, LLM judges and benchmarks, and iterating on prompts and pipelines against them.
• Comfortable getting deep into messy data: exploring it, troubleshooting issues, and transforming insights into measurable improvements.
• Strong Python and good engineering skills. You write clear, readable, tested code that others can take to production, even if you’re not the one owning the infrastructure.
• Able to work independently on ambiguous problems: framing the question, choosing the approach, and deciding on the acceptance criteria to ship.

None flagged.