Member of Technical Staff, Data Analysis and Evaluation

Who are we?

[Employer] is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems.

We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that.

We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. [Employer] is a team of researchers, engineers, designers, and more, who are all passionate about their craft.

We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us!

Role Overview:

As a Member of Technical Staff in Data Analysis and Evaluation, you will play a pivotal role in ensuring the quality, reliability, and performance of our large language models (LLMs). Your primary focus will be on designing and conducting data collection tasks, assessing and evaluating dataset quality, and analysing the robustness and generalisability of our models. You will work closely with cross-functional teams, including researchers, engineers, and data annotators, to conduct data-driven decision-making and improve the overall effectiveness of our AI systems.

This role combines expertise in statistics, experimental design incl. human annotators, and machine learning to ensure that our models are trained on high-quality data and perform reliably across diverse scenarios. You will contribute to [Employer]’s mission of advancing AI by ensuring our systems are robust, scalable, and impactful.

Please Note: We have offices in London, Paris, Toronto, San Francisco, and New York, but we also embrace being remote-friendly! There are no restrictions on where you can be located for this role.

Key Responsibilities:

• Design and oversee data collection tasks, including supporting human annotators and ensuring data quality.
• Develop and apply statistical methods to evaluate the quality and reliability of datasets.
• Analyse and assess the generalisability and robustness of ML systems across diverse use cases.
• Collaborate with teams to improve dataset quality and model performance.
• Train and fine-tune large language models (LLMs) on distributed training infrastructures.
• Conduct experiments to evaluate model performance and identify areas for improvement.

Qualifications:

• Extremely strong software engineering skills.

• Strong expertise in designing and conducting data collection tasks, including working with human annotators.
• Strong statistical skills and experience evaluating scientific experiments related to data collection and model performance.
• Experience analysing datasets with respect to their quality, biases, and suitability for training ML models.
• Hands-on experience training large language models (LLMs) on distributed training infrastructures.
• Familiarity with evaluating and improving the generalisability and robustness of ML systems.
• Proficiency in programming languages such as Python and ML frameworks (e.g., PyTorch, TensorFlow, JAX).
• Excellent communication skills to collaborate effectively with cross-functional teams and present findings.
• One or more papers at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP).

* This is neither an exhaustive nor necessary set of attributes. Even if none of these apply to you, but you believe you will contribute to [Employer], please reach out. We value diverse backgrounds and perspectives at [Employer].

Working Location:This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role.

Full-Time Employees at [Employer] enjoy these Perks:

• A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch.
• Full health and dental benefits, including a separate budget for mental health.
• RRSP matching, 401K, Pension Scheme.
• 100% Parental Leave top-up for up to 6 months, for either parent.
• Annual enrichment benefits:Arts & culture, fitness/wellness, quality time, and a workspace improvement credit.Education & learning stipend for conferences, courses, and coaching.

• 6 weeks of paid vacation (30 working days!)
• Budget for traveling to other offices if you are remote, plus an annual company offsite.

How and Where We Work:

• [Employer] is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon.
• For those in the office: a daily lunch program, plenty of snacks, and regular community and social events.
• For those not near an office: a co-working benefit so you can work alongside others in your city.
• Everyone receives a $500 home office stipend to set up your workspace properly.

If any of the above doesn’t line up exactly with your experience, we still encourage you to apply.

We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form, and we will work together to meet your needs.

We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider.

Beware of Scams: [Employer] will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the [Employer] careers page and LinkedIn only, with all communications from [Employer] employees coming from an @[Employer].com or @cw.[Employer] email alias. If jobs are viewed on other sites then please verify these through our official careers page.