At a Glance
- Tasks: Join us to enhance the safety and trustworthiness of AI models that impact real-world applications.
- Company: Cohere, a leading security-first enterprise AI company with a global presence.
- Benefits: Enjoy competitive pay, health benefits, generous vacation, and a supportive work environment.
- Other info: Remote-friendly culture with opportunities for career growth and collaboration across global teams.
- Why this job: Make a meaningful impact on AI safety while working with cutting-edge technology and passionate teams.
- Qualifications: Strong statistical and software engineering skills, plus experience in machine learning and data management.
The predicted salary is between 80000 - 100000 £ per year.
Cohere 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. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company co‑headquartered in Toronto and San Francisco, with key offices in London, New York City, Montreal, Seoul, Germany and Paris.
As a Member of Technical Staff in the Safety for Agents team, you will make a meaningful impact on the development of better, fairer, more trustworthy, and more secure Large Language Models (LLMs). Your primary focus will be on data generation, post‑training algorithms, and evaluation methods to ensure Safety in the next generation of models that can access external resources and take actions in the world. You will work closely with other cross‑functional machine learning teams and data annotation teams, and will also collaborate with product and policy teams. This role combines expertise in machine learning, ethical and responsible AI, experimental design, and data generation and management. It will require curiosity to tackle totally new scientific problems, engineering skills to implement the pieces we need to test solutions to these, and a desire to dive into messy data and results. You will be on a small team with a lot of autonomy and decision‑making power, responsible for making the next generation of LLMs better for society as a whole.
Please note: The existing team works in offices in London, Edinburgh, Paris, Toronto, and New York, but we also embrace being remote‑friendly! For this role you need to have ~50% working day overlap with UK/EU timezone (e.g. US East is fine) but there are otherwise no restrictions on where you can be located for this role.
You may be a good fit if you have:
- Strong statistical skills and experience evaluating scientific experiments related to data collection and model performance.
- Extremely strong software engineering skills.
- Strong expertise in designing and conducting data collection tasks, including working with human annotators.
- Experience analyzing 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 generalizability 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).
Full‑Time Employees at Cohere 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.
Cohere is remote‑friendly. We have offices in Toronto, San Francisco, New York City, London, Paris, Montreal, and more coming 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.
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 Accommodations Request Form, and we will work together to meet your needs.
Senior Member of Technical Staff, Safety for Agents employer: Cohere
Cohere is an exceptional employer that fosters a culture of innovation and collaboration, where every team member plays a vital role in shaping the future of AI. With a strong commitment to employee well-being, we offer generous benefits including a weekly lunch stipend, comprehensive health coverage, and six weeks of paid vacation, alongside opportunities for professional growth through education stipends and a supportive remote-friendly environment. Our London office, along with our global presence, provides a dynamic workplace that values diversity and inclusivity, making it an ideal setting for those passionate about advancing technology responsibly.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Member of Technical Staff, Safety for Agents
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We think you need these skills to ace Senior Member of Technical Staff, Safety for Agents
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at Cohere, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Cohere. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Cohere
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Cohere!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.