Machine Learning Engineer

Onhires

Europe +3 more

Remote

Who this role is best for

Aimed at mid-level Machine Learning Engineers who can own production systems and debug real-world issues.

Best fit for

Candidates with end-to-end ML system experience and a track record of shipping user-facing models
— “Experience building and shipping ML systems used by real users.”
Individuals comfortable with GPU-based workflows and Python-centric tooling
— “GPU-based training and inference systems.”

Things to consider

High expectations for production reliability and system-level thinking beyond algorithmic work
— “Ability to write production-quality code and think in systems, not scripts.”

How to stand out

Highlight end-to-end ML projects with measurable user impact in your resume
— “Ship quickly, measure outcomes, refine, and repeat.”
Demonstrate experience debugging production ML failures using real-world data
— “Debug model failures and system issues using real production signals.”
Emphasize technical mentorship experience or leadership in collaborative environments
— “Mentor and review work from other ML engineers.”
Showcase proficiency in PyTorch/JAX and GPU optimization techniques
— “PyTorch / JAX, GPU-based training and inference systems.”

Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · TeamLevel · Mid

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

What success looks like

accurate and reliable ML models
resolution of complex production issues
robust and scalable training and inference pipelines

Typical background

Experience building and shipping ML systems used by real usersStrong understanding of how modern ML models behave in production

Skills & requirements

Required

Machine LearningDeep LearningProduction ML SystemsModel Training And EvaluationSystem DebuggingCollaboration With Research And Engineering Teams

Preferred

Natural Language ProcessingComputer VisionAI Ethics

Stack & domain

PythonPyTorchJAXGpu-based Training And Inference SystemsML SystemsData PreparationTrainingEvaluationInferenceIterationModel FailuresSystem IssuesProduction SignalsLatencyCostReliabilitySafetyProduction ConstraintsTraining PipelinesInference PipelinesData PipelinesScalabilityMaintainabilityProblem SolvingCommunicationCollaborationAttention To DetailTechnical WritingCross-functional CollaborationAi-native Productivity ApplicationsAI WorkflowsConversationsCoordinationActionsContextExternal ServicesProactiveLong-horizon AIProduction SystemsEfficiencyTechnical GuidanceMentorshipIntegrationBusiness Goals

About the role

Original posting from Onhires via Ashby

We’re hiring on behalf of A1, a high‑talent team building the next generation of AI‑native productivity applications. Their mission is to replace repetitive digital work with AI that can reliably complete real tasks for everyday users.
Rather than building another chatbot, A1 is creating long‑running AI workflows that manage conversations, coordinate actions, maintain context, and interact with external services — all with minimal user input.
As a Machine Learning Engineer, you will own critical ML subsystems in production. This is a hands‑on, high‑impact role focused on depth and reliability at scale.
WHAT YOU’LL DO
Build core ML systems powering a proactive, long‑horizon AI product.
Own the full lifecycle: data preparation, training, evaluation, inference, iteration.
Turn research ideas into production systems that run reliably.
Debug model failures and system issues using real production signals.
Ship quickly, measure outcomes, refine, and repeat.
Collaborate closely with research, product, and engineering teams.
Mentor and review work from other ML engineers.
Work under real production constraints: latency, cost, reliability, safety.
TECH STACK
Python
PyTorch / JAX
GPU‑based training and inference systems
IDEAL BACKGROUND
Experience building and shipping ML systems used by real users.
Strong understanding of how modern ML models behave — and misbehave — in production.
Ability to write production‑quality code and think in systems, not scripts.
Independent ownership: driving work across the finish line.
Fast learner, clear communicator, iterative mindset.
EXPECTED OUTCOMES
ML models and systems consistently meet accuracy, latency, reliability, and efficiency targets.
Complex production issues are monitored, debugged, and resolved with minimal disruption.
Training, inference, and data pipelines are robust, scalable, and maintainable.
Measurable improvements in ML systems based on real‑world signals and user feedback.
Technical guidance and mentorship that raises the overall ML engineering standard.
Seamless integration of ML features into products that meet business goals.
HOW A1 WORKS
Our client A1 is a small, world‑class team with high talent density. They move quickly, make decisions collectively, and balance shipping high‑quality work with rapid learning. Structure, sound judgment, and the ability to execute independently are highly valued.
INTERVIEW PROCESS
3–4 interviews with technical team members.
Conducted virtually and/or onsite.
Transparent and efficient decision process.
Successful candidates will receive an offer to join a team building AI that delivers practical benefits to billions of users globally.

Source: Onhires careers (Ashby)