Senior Machine Learning Research Engineer – Benchmarking & Paper Replication

About the Position

Senior Machine Learning Research Engineer – Benchmarking & Paper Replication

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 UI design, managed services, product development, and software development.

At 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.

Responsibilities

• Read and analyze scientific / academic ML papers with understanding
• Identify promising methodologies and assess whether they are worth reproducing
• Build PoCs based on academic papers and open-source research implementations
• Debug, adapt, and reproduce research repositories under practical compute constraints
• Identify, curate, and construct benchmark datasets required to test specific methodologies
• Prepare ground truth data from large volumes of data to validate and test PoCs
• Build evaluation pipelines to compare replicated approaches against baselines
• Measure model performance critically and verify original paper claims
• Document findings, limitations, experiment results, and recommendations for next steps
• Collaborate with AI engineers and technical stakeholders to turn validated research into practical project direction

Requirements

• Strong machine learning fundamentals and hands-on experience with ML research or applied research projects
• Experience reading scientific / academic ML papers and understanding the methodology behind them
• Ability to dissect academic papers, debug open-source repositories, and replicate research results
• Experience identifying, curating, and constructing benchmark datasets for testing specific ML methodologies
• Ability to prepare ground truth data from large volumes of raw or semi-structured data
• Strong Python skills and hands-on experience with modern ML / deep learning frameworks
• Practical understanding of model evaluation, baselines, metrics, error analysis, and reproducibility
• Comfortable working under compute constraints and adapting research methods to practical limitations
• Able to work independently in an ambiguous R&D / PoC environment

Nice to Have

• Experience building rigorous evaluation pipelines from scratch
• Experience comparing original paper claims against baselines and alternative methods
• Experience with data annotation, dataset quality control, or benchmark design
• Experience with LLMs, agentic systems, or tool-based AI workflows
• Experience converting research workflows into reusable components, tools, or deployable skills for LLM-based systems
• Experience with MLOps, experiment tracking, model versioning, or reproducible ML pipelines
• Publications, PhD / research background, or strong open-source research contributions are a plus

We Offer

• Fully Remote work options
• Opportunity to work with a diverse and inclusive team
• Professional growth and development opportunities

About the Company

At Avenga, everyone matters. We provide equal opportunities in recruitment, career development, and leadership, regardless of race, ethnicity, gender identity, sexual orientation, disability, age, religion, or any other characteristic. We are committed to fostering a work environment where our diverse community of employees, candidates, and business partners actively shapes our growth. By bringing together people from different backgrounds and experiences, we build a workplace where everyone feels free to be themselves while honoring the boundaries of others.

Application Process

1. Application: Send us your application, we are ready to hear your story and explore how we can grow together.
2. Review: Our team carefully reviews every application with curiosity and care.
3. Talent Acquisition Interview: Meet our Talent team to talk about your goals and learn more about who we are, what we offer, and how we support our people.
4. Technical Interview: Dive deeper into your expertise and understand your strengths, how you solve problems, and how you think.
5. Customer Interview: For some roles, especially when our clients are closely involved, you might also have a conversation with them – it’s a chance for everyone to align and get to know each other better.
6. Job Offer: If we both feel it’s the right match, we will send you an offer to join us. And even if it’s not the time just yet, you’ll always hear back from us.
7. Welcome: Your new team is excited to meet you, support you, and build something great together. Let’s make your first day the start of a journey that shapes a career that matters.