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- Tasks: Develop and deploy machine learning models in a collaborative, innovative environment.
- Company: Join FactSet, a leading tech company transforming financial data solutions.
- Benefits: Enjoy competitive salary, remote work options, and professional growth opportunities.
- Why this job: Make a real impact in the finance world with cutting-edge technology and your coding skills.
- Qualifications: 5+ years in software engineering, AWS experience, and strong Python skills required.
- Other info: Be part of a diverse team recognised as one of the Best Places to Work in 2023.
The predicted salary is between 36000 - 60000 £ per year.
FactSet creates flexible, open data and software solutions for over 200,000 investment professionals worldwide, providing instant access to financial data and analytics that investors use to make crucial decisions. At FactSet, our values are the foundation of everything we do. They express how we act and operate, serve as a compass in our decision-making, and play a big role in how we treat each other, our clients, and our communities. We believe that the best ideas can come from anyone, anywhere, at any time, and that curiosity is the key to anticipating our clients' needs and exceeding their expectations.
FactSet is seeking a Software Engineer with experience in AWS cloud architecture, infrastructure deployment and maintenance. The Software Engineer will work with other engineers to serve applications with ML model implementations for NLP, classification and LLMs (Large Language Model). Necessary experience for this role would include knowledge of databases, APIs, Amazon Elastic Container Services (ECS) and other AWS services. This role is in the Data Solutions AI team and reports to the VP, Director of Engineering. The Software Engineer works with the team to develop a roadmap for management and growth of existing pipelines and infrastructure for serving ML and AI solutions.
What will you be doing?
- Manage and deploy various cloud-based infrastructure
- Participate in different projects as a software engineer
- Make sure to align with business needs
- Deliver clean, well-tested code that is reliable, maintainable, and scalable
- Deploy working solutions
- Develop dashboards and other visualizations for financial experts
- Ingest and analyse structured and unstructured data
- Develop processes for data collection, quality assessment, and quality control
- Deploy and maintain ML and NLP models
- Stay up to date with state-of-the-art approaches and technological advancement
- Collaborate with other Engineering teams
Who you are?
- You have BS or MS in Computer Science or Mathematics related field
- You have 5+ years of working experience as a software engineer
- You have experience with AWS and cloud-based infrastructure
- You have familiarity with ML, NLP and GenAI (including RAG, Prompt Engineering, Vector DBs)
- You have a successful history of writing production grade code and releasing in an enterprise environment
- You are a team player
- You have strong analytical skills
- You are fluent in English; you can communicate about complex subjects to non-technical stakeholders
- You are highly proficient in Python
- You are familiar with machine learning frameworks like sklearn and ML workflow
- You are familiar with NLP libraries and text preprocessing (nltk, SpaCy, etc.)
- Experience with OpenAI, Llama, and other large language model frameworks
- Prior experience working with unstructured data (text content, JSON records) including feature engineering experience from unstructured data
- Working with Agile development practices in a production environment
It is great if you have:
- Experience with AWS environment [SageMaker, S3, Athena, Glue, ECS, EC2]
- Experience with Agentic workflows and MCP
- Experience working with large volumes of data in a stream or batch processing environment
- Prior experience with Docker and API development
- Usage of MongoDB
- Familiarity with deep learning libraries (Keras, PyTorch, Tensorflow)
- Familiarity with big data tool chain (e.g. Pyspark, Hive)
- Experience with information extraction, parsing and segmentation
- Knowledge of ontologies, taxonomy resolution and disambiguation
- Experience in Unsupervised Learning techniques Density Estimation, Clustering and Topic Modelling
- Graph database experience (AWS Neptune, Neo4j)
Company Overview: FactSet helps the financial community to see more, think bigger, and work better. Our digital platform and enterprise solutions deliver financial data, analytics, and open technology to more than 8,200 global clients, including over 200,000 individual users. Clients across the buy-side and sell-side, as well as wealth managers, private equity firms, and corporations, achieve more every day with our comprehensive and connected content, flexible next-generation workflow solutions, and client-centric specialized support. As a member of the S&P 500, we are committed to sustainable growth and have been recognized among the Best Places to Work in 2023 by Glassdoor as a Glassdoor Employees' Choice Award winner. At FactSet, we celebrate difference of thought, experience, and perspective. Qualified applicants will be considered for employment without regard to characteristics protected by law.
Software Engineer - (Machine Learning Engineer) - Hybrid employer: FactSet
Contact Detail:
FactSet Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Software Engineer - (Machine Learning Engineer) - Hybrid
✨Tip Number 1
Network like a pro! Reach out to current employees at FactSet on LinkedIn or other platforms. Ask them about their experiences and any tips they might have for landing the job. Personal connections can make a huge difference!
✨Tip Number 2
Prepare for technical interviews by brushing up on your coding skills and ML concepts. Use platforms like LeetCode or HackerRank to practice coding challenges. Remember, showing off your problem-solving skills is key!
✨Tip Number 3
Show your passion for ML and AI during interviews. Share personal projects or contributions to open-source that relate to the role. This not only demonstrates your skills but also your enthusiasm for the field!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining the team at FactSet.
We think you need these skills to ace Software Engineer - (Machine Learning Engineer) - Hybrid
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Software Engineer role. Highlight your AWS experience, ML projects, and any relevant coding languages like Python. We want to see how you fit into our team!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Share your passion for machine learning and how your background makes you a great fit for FactSet. Don’t forget to mention why you’re excited about working with us and our values.
Showcase Your Projects: If you've worked on any cool projects, especially those involving ML or cloud infrastructure, make sure to include them. We love seeing real-world applications of your skills, so don’t hold back!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets to the right people. Plus, it shows you’re serious about joining our team at FactSet!
How to prepare for a job interview at FactSet
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, especially AWS services and machine learning frameworks. Brush up on your knowledge of Python, NLP libraries, and cloud infrastructure to demonstrate your expertise.
✨Showcase Your Projects
Prepare to discuss specific projects where you've implemented ML models or worked with cloud-based infrastructure. Highlight your role, the challenges faced, and how you overcame them. This will show your practical experience and problem-solving skills.
✨Understand the Company Culture
Familiarise yourself with FactSet’s values and mission. Be ready to explain how your personal values align with theirs. This can help you stand out as a candidate who not only has the technical skills but also fits well within their team.
✨Ask Insightful Questions
Prepare thoughtful questions about the team dynamics, ongoing projects, and future goals of the Data Solutions AI team. This shows your genuine interest in the role and helps you assess if the company is the right fit for you.
