ML Research engineer

Kennedy Pearce Consulting; Deutschland; Remote; EUR 70.000 - 100.000; Vollzeit

Zusammenfassung

An innovative AI company in Germany seeks an ML Research Engineer to enhance manufacturing efficiency through advanced AI algorithms. This pivotal role involves direct impact on real-world processes, utilizing a small data approach to deliver measurable results. The ideal candidate will possess expertise in Bayesian optimization and strong Python skills, ready to thrive in a dynamic startup environment.

Leistungen

30 vacation days

Flexible work hours

Professional development opportunities

Remote-first culture

Qualifikationen

• Expertise in Bayesian Optimization and Gaussian Processes.
• Hands-on experience with Few-Shot Learning and Reinforcement Learning.
• Strong foundation in statistics, probability theory, and small datasets.

Aufgaben

• Collaborate on Bayesian optimization and strategic technical decisions.
• Independently prototype and iterate AI solutions.
• Develop algorithms suggesting batches of experiments.

Kenntnisse

Bayesian Optimization

Gaussian Processes

Few-Shot Learning

Reinforcement Learning

Optimization under uncertainty

Multi-objective optimization

Python

Git

Tools

scikit-learn

GPyTorch

GPflow

Jobbeschreibung

ML Research Engineer - Manufacturing Optimization

About the Role

We 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 molding.

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

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.

What We Offer

Compensation & Equity

Salary: €70,000 - €100,000 (based on experience and expertise)

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

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

Follow us on social media for updates on our latest opportunities, market trends and what it's like to work with us.