Senior Machine Learning Engineer

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 Senior Machine Learning Engineer operating at Senior Member of Technical Staff level, you will independently own critical ML subsystems running in production.

You will take ambiguous technical problems, design practical solutions and deliver systems that operate reliably at scale.

This is a hands-on, high-impact individual contributor role focused on technical depth, production ownership and measurable product outcomes.

What you will do

• Build core Machine Learning systems powering a proactive, long-horizon AI product.

• Own work end-to-end across data preparation, training, evaluation, inference and continuous iteration.

• Transform research concepts into reliable systems operating in production.

• Diagnose model failures and system-level issues using real production signals.

• Work iteratively: ship solutions, measure outcomes, refine them and repeat.

• Collaborate closely with research, product and engineering teams to deliver measurable user impact.

• Mentor other Machine Learning engineers and review their work through technical judgment and practical example.

• Operate under real production constraints, including latency, cost, reliability and safety.

Technology stack

• Python

• PyTorch / JAX

• GPU-based training and inference systems

What we are looking for

• Proven experience building and shipping Machine Learning systems used by real users.

• Strong understanding of how modern Machine Learning models behave—and fail—in production environments.

• Ability to write high-quality production code and think in complete systems rather than isolated scripts.

• Strong ownership and the ability to work independently from an ambiguous problem through production delivery.

• Experience working across data, model training, evaluation and inference.

• Ability to communicate clearly, learn quickly and improve systems through continuous iteration.

• Sound technical judgment when balancing model quality, latency, cost, reliability and safety.

• Ability to mentor peers and raise the technical standard of a Machine Learning team.

What success looks like

• Production ML models and systems consistently meet accuracy, latency, reliability and efficiency expectations.

• Complex production problems are monitored, diagnosed and resolved with minimal disruption.

• Training, inference and data pipelines remain robust, scalable and maintainable over time.

• ML systems demonstrate measurable improvement based on real-world signals and user feedback.

• Other engineers receive effective mentorship and technical guidance.

• ML capabilities integrate seamlessly into the wider product and support defined business objectives.

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 Machine Learning, research, engineering and product 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 take ownership, bring structure to ambiguous problems and execute independently.

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 and ownership standards rather than filling a predetermined number of seats.

Recruitment process

The standard recruitment process consists of up to four stages:

1. Technical assessment, where relevant to the candidate’s background.

2. HR interview.

3. One or more technical interviews.

4. 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.