Senior Machine Learning Research Engineer

This is us

At Avenga, we believe that human creativity empowers technology that matters. Operating globally, our 6000+ specialists provide a full spectrum of services, including business and tech advisory, enterprise solutions, CX, UX and Ul design, managed services, product development, and software development.

This is the jo

bAt the intersection of applied ML research and real-world AI product development, we are looking for a Senior Machine Learning Research Engineer to help explore, reproduce, benchmark and validate cutting-edge ML methodologies

.You will work in an R&D / PoC environment where the main focus is not only to build models, but to understand research papers, replicate promising approaches, evaluate them against strong baselines, and prepare reliable ground truth data for validation

.This role is a strong fit for someone who enjoys reading academic papers, debugging open-source research repositories, building benchmark datasets from scratch, and turning research ideas into working, measurable prototypes under practical compute constraints

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This is y

• ouStrong machine learning fundamentals and hands-on experience with ML research or applied research projec
• tsExperience reading scientific / academic ML papers and understanding the methodology behind th
• emAbility to dissect academic papers, debug open-source repositories, and replicate research resul
• tsExperience identifying, curating, and constructing benchmark datasets for testing specific ML methodologi
• esAbility to prepare ground truth data from large volumes of raw or semi-structured da
• taStrong Python skills and hands-on experience with modern ML / deep learning framewor
• ksPractical understanding of model evaluation, baselines, metrics, error analysis, and reproducibili
• tyComfortable working under compute constraints and adapting research methods to practical limitatio
• nsAble to work independently in an ambiguous R&D / PoC environme

ntNice-to-have skill

• s:Experience building rigorous evaluation pipelines from scrat
• chExperience comparing original paper claims against baselines and alternative metho
• dsExperience with data annotation, dataset quality control, or benchmark desi
• gnExperience with LLMs, agentic systems, or tool-based AI workflo
• wsExperience converting research workflows into reusable components, tools, or deployable skills for LLM-based syste
• msExperience with MLOps, experiment tracking, model versioning, or reproducible ML pipelin
• esPublications, PhD / research background, or strong open-source research contributions are a pl

usThis is your ro

• leRead and analyze scientific/academic ML papers with understandi
• ngIdentify promising methodologies and assess whether they are worth reproduci
• ngBuild PoCs based on academic papers and open-source research implementatio
• nsDebug, adapt, and reproduce research repositories under practical compute constrain
• tsIdentify, curate, and construct benchmark datasets required to test specific methodologi
• esPrepare ground truth data from large volumes of data to validate and test Po
• CsBuild evaluation pipelines to compare replicated approaches against baselin
• esMeasure model performance critically and verify the original paper clai
• msDocument findings, limitations, experiment results, and recommendations for next ste
• psCollaborate with AI engineers and technical stakeholders to turn validated research into a practical project directi