Senior/Lead Machine Learning Engineer

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.

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.

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.

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.

Use AI coding tools effectively and stay accountable for the output: understand, explain, and defend everything you ship.

Communicate clearly in spoken and written English, including explaining technical trade-offs to both technical and non-technical audiences

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.

Stage 1. Talent Partner (30-45 min)

Stage 2. Take-home challenge (flexible) + Technical Discussion with the team (1h)

Stage 3. Technical design exercise and situational questions in session w/ two ML Engineers (1h)

Stage 4. Behavioral interview w/ Engineering leadership (1h)

Offer

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