Member of Technical Staff, Machine Learning
Summary
Build and improve core ML components for an AI-native assistant that handles long-running tasks, retains context, and interacts with external tools in production systems.
About the product
Our client is developing an AI-native assistant designed to support everyday communication, organization, errands and complex workflows with minimal user input.
The product must reliably manage long-running processes, retain context, interact with external tools and complete real-world tasks despite the non-deterministic nature of modern AI models. The company’s objective is to make everyday activities significantly faster and easier for users.
About the role
As a Member of Technical Staff specializing in Machine Learning, you will build and improve core ML components used in a production AI product.
You will work with real production systems from the beginning, developing an understanding of how large-scale Machine Learning operates outside controlled research environments.
This position is intended for an engineer who wants to strengthen their systems judgment by shipping, debugging and continuously improving real-world ML solutions.
What you will do
Build and improve Machine Learning components across data, training, evaluation and inference.
Fine-tune and adapt models as part of larger production systems.
Implement evaluation and testing methods to understand and measure model behavior.
Help build and maintain pipelines for real-world and synthetic data.
Diagnose model failures, performance problems and production incidents.
Deliver improvements iteratively and learn from real user feedback.
Work closely with senior Machine Learning engineers, product teams and other technical specialists.
Operate under real production constraints, including latency, cost, reliability and safety.
Technology stack
Python
PyTorch / JAX
GPU-based production Machine Learning systems
What we are looking for
Strong foundations in Machine Learning and modern neural network architectures.
Some hands-on experience training, fine-tuning or deploying Machine Learning models.
Ability to write production-quality code and learn new tools quickly.
Curiosity, openness to feedback and a strong willingness to learn from production systems.
Ability to work through ambiguity with guidance and gradually take ownership of increasingly complex problems.
A delivery-oriented mindset focused on shipping, measuring, iterating and continuously improving systems.
What success looks like
Production Machine Learning models consistently meet accuracy, latency and reliability expectations.
Production issues are detected quickly, diagnosed effectively and resolved at the root-cause level.
Data pipelines, training workflows and inference systems are robust, reproducible and maintainable.
ML-powered functionality is delivered through effective collaboration with engineering, product and research teams.
Improvements to models and systems are driven by real-world signals, measurable outcomes and user feedback.
Compensation and employment
The position is offered under an employment contract.
The company does not publish a fixed external salary range. Compensation is assessed individually based on:
professional experience and technical capability,
scope of responsibility,
location and relevant market benchmarks,
expected impact on the product and organization.
Candidates may share their expected compensation at the beginning of the recruitment process. The compensation package consists of a base salary and equity, with flexibility for exceptional candidates.
A company laptop will be provided where required for the role.
Remote work and global collaboration
The company operates as a remote-first, globally distributed organization.
There is no fixed company-wide working schedule and no requirement to follow one specific time zone. Team members are expected to maintain sufficient working-hours overlap with their immediate colleagues to collaborate effectively.
The successful candidate will work from Poland and collaborate with senior Machine Learning engineers, product teams and engineering specialists located across different regions.
Poland-based employees join existing global teams rather than a separate local team. The exact reporting line and hiring manager will be confirmed during the recruitment process.
How the team works
The company believes that outstanding products are built by small, highly capable and hands-on teams. Decisions are made collaboratively, while individuals are expected to bring structure to ambiguous problems, exercise sound judgment and develop increasing independence.
The team moves quickly while balancing production quality, experimentation and continuous learning from real-world usage.
There is no fixed hiring quota for this position. The company is focused on identifying engineers who meet its technical standards and demonstrate strong growth potential rather than filling a predetermined number of seats.
Recruitment process
The standard recruitment process consists of up to four stages:
Technical assessment, where relevant to the candidate’s background.
HR interview.
One or more technical interviews.
Founder or leadership interview.
Particularly strong candidates may be fast-tracked directly to the technical interview stage based on their experience and previous work.
Applications are evaluated by members of the technical team. Interviews may be conducted virtually and, where relevant, onsite. The exact format and interviewers may vary.
The company aims to make decisions efficiently and provide candidates with a prompt outcome.