Senior/Lead Machine Learning Engineer
Pubblicato il 28-10-2026 - Mimica Automation in Montà
What we are building
Le informazioni riportate di seguito illustrano i requisiti del ruolo, l'esperienza richiesta ai candidati e le qualifiche associate.
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. Not because companies don’t want to become more efficient, but because they don’t know where to start.
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. Mimica is recognized as a Top 100 AI Start-up by Accel and an industry Leader by the Everest Group.
Our team
Behind our platform is a team of exceptionally talented people, remote-first and globally distributed across 16+ countries. We're looking for curious, collaborative people excited to solve complex problems. You'll join a fast-paced environment built on high ownership, bottom-up thinking, and transparent, open communication – helping some of the world's leading organizations transform the way work gets done with AI.
Our approach to engineering
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- 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.
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.
Part of your day-to-day
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- 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)
What we’re looking for
Primarily, we’re looking for an individual with an experiment- and data-driven researcher mindset with good coding habits. This includes:
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- 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