About Mimica
Mimica is an AI-powered process intelligence platform enabling enterprises and individuals to reclaim their most precious resource: time. We've all seen repetitive tasks and redundant processes quietly drain productivity and creativity. Mimica automatically analyzes your team’s work, then generates the AI agents to execute it. Our software turns everyday work into data-backed decisions, helping companies remove process bottlenecks, automate the right tasks, and upskill teams with precision and confidence.
We’ve raised $26 million in Series B financing and are scaling fast. Backed by investors including Khosla Ventures and Paladin Capital, our proprietary AI technology is trusted by Fortune 500 companies to accelerate transformation.
Our Team
Behind our platform is a team of exceptionally talented people, remote-first and globally distributed across 16+ countries. You'll join a fast-paced environment built on high ownership, bottom-up thinking, and transparent, open communication.
Our Approach to Engineering
- 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
Your Mission
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. You’ll own key parts of our ML pipelines end to end, making them more accurate, cost-efficient, and trustworthy. Along the way, you’ll build evaluations and LLM judges, run experiments, and transform promising results into validated production improvements.
Day-to-Day Responsibilities
- 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, 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 (e.g., experiment reports, design docs).
What we offer
- 💰 Generous compensation + stock options - aligned with our internal framework, market data, and individual skills.
- 🏢 Distributed work: Work from anywhere - fully remote, in our hubs, or a mix.
- 💻 Company-issued laptop, remote setup stipend, and co-working budget
- 📍 Flexible schedules and location
- ☀️ Ample paid time off, in addition to local public holidays
- 🍼 Enhanced parental leave
- 🧘♀️ Health & retirement benefits
- 📖 Annual learning & development budget
- 🌴 Annual workaways and regular virtual & in-person socials
- 🌍 Opportunity to contribute to groundbreaking projects that shape the future of work
What we’re looking for
- Solid machine learning/data science background: you understand experimental design, data splits, metrics, bias, and statistical significance.
- 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.
- 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.
- Able to work independently on ambiguous problems: framing the question, choosing the approach, and deciding on the acceptance criteria to ship.
- Use AI coding tools effectively and stay accountable for the output.
- Communicate clearly in spoken and written English.
Bonus / Nice to have
- Experience with classical ML or deep learning model development.
- Experience with image/OCR pipelines or face-related anonymization.
- Experience shipping end-to-end ML features in a startup or scale-up environment.
