Machine Learning research engineer

KennedyPearce Consulting; Dortmund; Remote; EUR 75.000 - 95.000; Vollzeit

Zusammenfassung

A specialized consulting firm is seeking an ML Research Engineer to optimize manufacturing processes using AI algorithms. This remote-first role focuses on developing innovative solutions, with opportunities for significant equity and ownership. Ideal candidates should possess a strong foundation in statistics, hands-on experience with machine learning techniques, and the ability to collaborate with stakeholders. Enjoy flexible hours, a collaborative team environment, and substantial professional development opportunities.

Leistungen

30 vacation days

Professional Development training

Remote work flexibility

Qualifikationen

• Expertise in Bayesian Optimization and Gaussian Processes is required.
• Hands-on experience with Few-Shot Learning and Reinforcement Learning is essential.
• Ability to read and implement algorithms from academic literature.

Aufgaben

• Develop and collaborate on Bayesian optimization algorithms.
• Prototype AI solutions independently focusing on speed-to-market.
• Engage with customers to understand practical constraints.

Kenntnisse

Bayesian Optimization

Hands-on experience with Few-Shot Learning

Strong foundation in statistics

Tools

Python

scikit-learn

Git

Jobbeschreibung

ML Research Engineer – Manufacturing Optimization

Location: Remote-first (Germany preferred)

Overview

Our client are pioneering AI-driven optimization for manufacturing processes, tackling one of the industry’s biggest challenges: running machines efficiently despite skills gaps. Backed by recent funding, the team is expanding to make real impact across industries, from sheet-metal processing to injection moulding. Unlike others requiring thousands of data points, our “small data” approach helps manufacturers optimize processes with just a handful of experiments, delivering measurable improvements in efficiency, quality, and sustainability.

The Opportunity

We are seeking our first dedicated ML Research Engineer to join the leadership team in building the core AI capabilities that define the product. You will research and implement advanced AI algorithms that directly impact manufacturing efficiency and sustainability at scale.

What Makes This Role Unique

• Direct Impact: Optimize real manufacturing processes, reducing waste and improving efficiency.
• Technical Innovation: Apply small data approaches using Bayesian optimization to enhance machine performance.
• Equity & Ownership: Meaningful ownership (1%+) as a first key technical hire.
• Growth Potential: Opportunity to eventually lead the AI / ML team.
• Real-World Application: Work directly with manufacturing customers, not just theoretical problems.

Core Responsibilities

• Algorithm Development: Collaborate with leadership on Bayesian optimization and strategic technical decisions.
• Literature Review & Research: Review cutting-edge research in Bayesian optimization, batch acquisition functions, and transfer learning for small data applications.
• Rapid Prototyping: Independently prototype and iterate AI solutions with speed-to-market focus.
• Batch Optimization: Develop algorithms that suggest batches of experiments in each step.
• Knowledge Transfer: Enable transfer learning across machines to minimize experiments.
• Data Quality Systems: Build robust validation and cleanup pipelines.
• Integration of Process Knowledge: Apply domain knowledge to machining, injection molding, and welding processes.
• Customer Interaction: Occasionally engage with customers to understand real-world constraints.

Required Technical Skills :

Machine Learning & Optimization

• Expertise in Bayesian Optimization and Gaussian Processes (implementation and theory)
• Hands-on experience with Few-Shot Learning and Reinforcement Learning
• Knowledge of optimization under uncertainty and multi-objective optimization
• Ability to read and implement algorithms from academic literature
• Experimental mindset: design of experiments and algorithm benchmarking
• Strong foundation in statistics, probability theory, and small datasets

Programming & Development

• Advanced proficiency in Python (libraries such as scikit-learn, GPyTorch, GPflow)
• Rapid prototyping, iterative development, and Git / collaborative practices

Highly Desirable

• Experience with Bayesian Neural Networks, batch / multitask optimization, transfer learning, meta-learning
• Implementation of algorithms from research papers
• Background in manufacturing or industrial process optimization
• Experience with web development (FastAPI, MongoDB, React / Angular) and cloud platforms

Personal Attributes & Work Style

• Research Curiosity: Passion for innovation and translating ML research into practical solutions.
• Startup Mindset: Thrive in fast-paced, resource-constrained environments, developing deployable solutions independently. Comfortable with ambiguity, risk, and shifting priorities.
• Communication & Impact: Ability to explain complex technical concepts to non-technical stakeholders. Motivated by solving real-world manufacturing problems.

On Offer

Compensation & Equity

• Equity: Substantial package

Benefits & Culture

• Remote-first: Flexible hours, work from anywhere (Germany preferred)
• Time Off: 30 vacation days, with additional flexibility
• Professional Development: Training and courses provided
• Equipment & Setup: All tools required for effective remote work

Work Environment

• Collaborative, small team where your voice matters
• Growth-oriented: Shape engineering culture as the first technical hire
• Customer-connected: See your impact through occasional customer interactions
• Team-building: Quarterly and yearly company activities and strategy sessions