Machine Learning Platform Engineer

Machine Learning Platform Engineer

About the Company

Our client is a remote-first global AI product company building a proactive smart assistant for everyday users. The product brings intelligence to conversations, everyday tasks, organization, and workflows while requiring minimal prompting.

The platform is designed to reliably execute long-running workflows, retain persistent context, and complete real-world tasks. It must support multi-step reasoning, interact with external tools, and remain dependable despite the non-deterministic nature of modern AI models.

The goal is to make everyday tasks easier, faster, and more intuitive through practical AI experiences used by people around the world.

About the Role

As a Machine Learning Platform Engineer, you will build the infrastructure and systems that power the company’s AI capabilities.

You will design and operate the systems behind the AI stack—from model training and evaluation to deployment, inference, observability, and continuous improvement.

You will work closely with AI engineers, researchers, and product engineers to turn models into reliable, scalable, and cost-efficient production systems. You will build the platforms, tooling, and infrastructure that enable the team to experiment quickly and bring AI capabilities into production with confidence.

Your Responsibilities

• Build and operate the ML infrastructure and platforms powering production AI products.

• Design systems for model training, evaluation, deployment, inference, and experimentation.

• Build and optimize model-serving and inference infrastructure for high-throughput, low-latency workloads.

• Improve the reliability, scalability, latency, throughput, and cost efficiency of AI systems.

• Develop reliable pipelines for data preparation, training, evaluation, model releases, and continuous improvement.

• Build platforms and tooling that enable AI engineers and researchers to experiment, evaluate, and ship models faster.

• Develop evaluation and benchmarking infrastructure to measure model quality, performance, and regressions.

• Build production observability, monitoring, tracing, and alerting for AI and ML workloads.

• Identify bottlenecks across the ML stack and continuously improve system performance.

• Work closely with AI engineers, researchers, and product teams to turn evolving model requirements into production-ready infrastructure.

Tech Stack

• Python

• PyTorch / JAX

• LLM and ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM

• Cloud infrastructure

• Distributed systems

• ML and data pipelines

• Workflow orchestration

• GPU infrastructure and performance tooling

• Vector databases and retrieval infrastructure

Ideal Experience

• Strong software engineering fundamentals and experience building production systems.

• Experience building ML infrastructure, ML platforms, or production machine learning systems.

• Hands-on experience with model deployment, inference, evaluation, or data pipelines.

• Experience operating ML or AI workloads in production.

• Strong understanding of distributed systems, scalability, observability, and system reliability.

• Familiarity with GPU-based training or inference infrastructure.

• Ability to write clean, maintainable, production-quality code.

• Ability to diagnose performance, reliability, and cost bottlenecks across the ML stack.

• Comfort working in ambiguous, fast-moving environments.

• Bias toward ownership, experimentation, and continuous improvement.

Expected Outcomes

• AI infrastructure reliably supports production workloads at scale.

• Models can be trained, evaluated, deployed, and improved efficiently.

• Inference systems deliver strong latency, throughput, reliability, and cost efficiency.

• ML pipelines are reproducible, observable, maintainable, and robust.

• Model and infrastructure regressions are detected quickly and diagnosed efficiently.

• Common ML infrastructure capabilities become reusable platform components instead of being rebuilt for every AI product.

• The AI stack can evolve rapidly as new models, architectures, and inference techniques emerge.

Employment and Working Model

• Polish employment contract.

• Fully remote position for candidates based in Poland.

• You will join an existing global team and collaborate with colleagues across different regions.

• There is no fixed company-wide schedule or requirement to work in one specific time zone.

• Sufficient overlap with your immediate teammates is required for effective collaboration.

• A company laptop will be provided where required for the role.

• The compensation package consists of a base salary and equity.

• Compensation is assessed individually based on experience, technical capability, expected impact, and relevant market benchmarks.

• Candidates are invited to share their expected gross monthly salary in PLN.

Interview Process

The standard recruitment process consists of:

1. Technical assessment, where relevant.

2. HR interview.

3. Technical interview or interviews.

4. Founder or leadership interview.

The process normally includes three and no more than four interviews. Particularly strong candidates may be fast-tracked directly to the technical stages. The exact interviewers and assessment format may vary.

Applications are reviewed by the technical team. Interviews are conducted remotely, and candidates can expect a transparent and efficient decision-making process.

The recruitment process is managed by Talentica on behalf of the client.