Founding Machine Learning Engineer

Clera

Munich, Germany

On-site

Who this role is best for

A natural match if you have experience in building production ML systems and working with agent environments. The role is based in Munich and demands hands-on technical leadership in an early-stage, collaborative setting.

Best fit for

Candidates with a track record in post-training systems and agent environment design
— “Experience building agent environments, reinforcement learning systems, replay tools, or trajectory debugging infrastructure is valuable.”
Individuals who can shape technical direction and manage team growth in ambiguous settings
— “Ability to connect research with production, take ownership, and work effectively in an ambiguous environment.”
Professionals with 3+ years of ML engineering experience in production systems
— “At least 3 years of experience in machine learning engineering, building or delivering production machine learning systems.”

Things to consider

On-site presence in Munich is required, with no remote flexibility implied
— “On-site in Munich, Germany.”
Visa sponsorship is not available, which may limit international applicants
— “Visa sponsorship is not available.”

How to stand out

Highlight your experience with trajectory data and reward structures in your resume
— “Strong understanding of trajectory data, reward structures, tool use, and agent environment design.”
Showcase projects involving training data pipelines and model evaluations
— “Hands-on experience with post-training machine learning, evaluation frameworks, and training data pipelines, including structuring, filtering, and scoring data.”

Pace · Fast PacedCollaboration · HighAutonomy · HighDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

builds reliable agent environments
establishes validation and provenance tracking
sets machine learning roadmap priorities

Typical background

3+ years of experience in machine learning engineering

Skills & requirements

Required

Machine Learning EngineeringProduction Software EngineeringPost-training Machine LearningEvaluation FrameworksTraining Data PipelinesTrajectory DataReward StructuresTool UseAgent Environment Design

Preferred

Reinforcement Learning SystemsReplay ToolsTrajectory Debugging Infrastructure

Stack & domain

PythonMachine Learning EngineeringPost-training Machine LearningEvaluation FrameworksTraining Data PipelinesTrajectory DataReward StructuresAgent Environment DesignReinforcement Learning SystemsReplay ToolsTrajectory Debugging InfrastructureAbility To Connect Research With ProductionOwnershipEffectiveness In Ambiguous EnvironmentAI SystemsMachine Learning

About the role

Original posting from Clera via Ashby

ABOUT THE ROLE
An early-stage AI systems company is seeking a Founding Machine Learning Engineer to build its machine learning function and shape the technical direction of post-training and agent systems. You will work closely with the founding team and help set priorities for experiments, infrastructure, and future team growth.
WHAT YOU'LL DO
Structure, filter, and score experimental trajectories for training data pipelines.
Design and implement evaluations and benchmarks for model reasoning, planning, and experimental progress.
Build reliable agent environments, tool interfaces, observability systems, and replay infrastructure.
Establish validation and provenance tracking to improve trajectory and data quality.
Set machine learning roadmap priorities across systems, experiments, and hiring.
Guide the technical direction of the machine learning team as it grows.
WHAT WE'RE LOOKING FOR
At least 3 years of experience in machine learning engineering, building or delivering production machine learning systems.
Production software engineering experience with Python and systems-level programming for machine learning infrastructure.
Hands-on experience with post-training machine learning, evaluation frameworks, and training data pipelines, including structuring, filtering, and scoring data.
Strong understanding of trajectory data, reward structures, tool use, and agent environment design.
Experience building agent environments, reinforcement learning systems, replay tools, or trajectory debugging infrastructure is valuable.
Ability to connect research with production, take ownership, and work effectively in an ambiguous environment.
COMPENSATION & BENEFITS
Annual base salary: $100,000 to $250,000 USD. Visa sponsorship is not available.
LOCATION
On-site in Munich, Germany.

Source: Clera careers (Ashby)