At a Glance
- Tasks: Lead the ML/AI function, architecting multi-modal AI systems for real-world workflows.
- Company: Saris AI, an innovative startup revolutionising the banking industry with cutting-edge AI.
- Benefits: Competitive salary, premium benefits, equity package, and a dynamic work environment.
- Other info: Work alongside a stellar team backed by top Silicon Valley VCs.
- Why this job: Join us to tackle complex challenges and make a real impact in the trillion-dollar banking sector.
- Qualifications: 8+ years in ML/AI engineering, with experience leading projects from inception to production.
The predicted salary is between 80000 - 100000 £ per year.
About Saris AI
We're a San Francisco, Montreal and Toronto based applied AI startup that's building the future of work in the banking industry. We are tackling a $100 billion/yr problem, doubling every quarter and pushing the boundaries of what’s possible with multi-turn AI agentic systems.
Our goal is to tackle the type of automation problems that require long-context reasoning, tool orchestration across legacy systems, and strict compliance loops: the ones without known answers. We’ve shipped real agents that handle real customer workflows in production. With a growing customer base and live deployments, we’re scaling up fast and looking for deeply technical builders who want to have outsized impact early.
Your mission is to:
- Own and lead the ML/AI function end-to-end, setting technical direction and standards across the company.
- Architect and guide the development of multi-modal, agentic AI systems powering real-world workflows.
- Define and oversee evaluation frameworks, datasets, and performance metrics to continuously improve agent quality.
- Drive productionization of ML systems, ensuring reliability, scalability, and compliance in real-world environments.
- Build and mentor a high-performing ML team over time, setting best practices across modeling, experimentation, and deployment.
Who You Are:
- 8+ years of experience in ML/AI engineering, including time as a technical lead or manager.
- Proven track record of leading ML initiatives end-to-end, from problem definition to production deployment.
- Deep experience with LLMs and/or agentic systems, ideally in real-world, customer-facing applications.
- Strong understanding of ML fundamentals (deep learning, transformers, model evaluation, tradeoffs).
- Experience scaling ML systems in production, including monitoring, iteration, and reliability.
- Demonstrated ability to lead engineers, influence architecture decisions, and drive technical direction.
- Comfortable operating in early-stage, ambiguous environments with high ownership.
- Strong communication skills with the ability to translate complex ML concepts into clear decisions.
Bonus Points If You:
- Have experience building agentic systems, orchestration layers, or long-context reasoning systems.
- Are comfortable across the stack (data → modeling → infra → APIs).
- Have worked with both open-source and closed LLMs, including fine-tuning or retrieval systems (RAG).
- Have a strong product mindset and care deeply about real-world impact, not just model performance.
Why Join Saris AI?
- Join us in building the future of work for the trillion-dollar banking industry using cutting edge AI technology.
- Tackle ambiguous technical challenges with no clear answers.
- Competitive compensation with premium benefits and equity package.
- Work with a stellar team of engineers, builders, and leaders; including repeat YC founders with a successful exit (Ready Education).
- We already have production agents live with revenue-generating customers.
- Our team is backed by Tier 1 Silicon Valley VCs.
ML Engineering Lead in San Francisco employer: Saris
Saris AI is an exceptional employer for those looking to make a significant impact in the banking industry through cutting-edge AI technology. With a dynamic work culture that embraces ambiguity and innovation, employees benefit from competitive compensation, premium benefits, and equity packages, while also having the opportunity to grow alongside a talented team of engineers and leaders. Join us in shaping the future of work and tackling complex challenges in a fast-paced startup environment.
StudySmarter Expert Advice🤫
We think this is how you could land ML Engineering Lead in San Francisco
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Saris!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like ML Engineering Lead at Saris.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Saris.
✨Apply Directly through Our Website
When you find a suitable opening like ML Engineering Lead at Saris, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace ML Engineering Lead in San Francisco
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!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Saris, 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 Saris. 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 Saris
✨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!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨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 Saris!
✨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.
