Apollo
Senior Machine Learning Engineer II
Job Summary
This role involves building, evaluating, deploying, and iterating on scalable machine learning models and systems across Apollo products such as Search, Recommendations, and Content Generation. The candidate should have extensive experience in deploying ML models, working with cloud environments, and expertise in frameworks like TensorFlow, PyTorch, or Scikit-learn. The position emphasizes designing data pipelines, automating monitoring, and improving ML stacks while collaborating with stakeholders. It's suited for a highly experienced professional with strong technical skills, analytical problem-solving abilities, and a passion for AI innovation and continuous improvement.
Required Skills
Job Description
Apollo.io is the leading go-to-market solution for revenue teams, trusted by over 500,000 companies and millions of users globally, from rapidly growing startups to some of the world's largest enterprises. Founded in 2015, the company is one of the fastest growing companies in SaaS, raising approximately $250 million to date and valued at $1.6 billion. Apollo.io provides sales and marketing teams with easy access to verified contact data for over 210 million B2B contacts and 35 million companies worldwide, along with tools to engage and convert these contacts in one unified platform. By helping revenue professionals find the most accurate contact information and automating the outreach process, Apollo.io turns prospects into customers. Apollo raised a series D in 2023 and is backed by top-tier investors, including Sequoia Capital, Bain Capital Ventures, and more, and counts the former President and COO of Hubspot, JD Sherman, among its board members.
Working at Apollo:
We are a remote-first inclusive organization focused on operational excellence. Our way of working ensures clear expectations and an environment to do your best work with ample reward.
Your Role & Mission:
As a Senior Machine Learning Engineer on the Intelligence team, you will be responsible for building and productionizing Machine Learning (ML) models and other smart algorithms for various Apollo products. These products may include Search, Recommendations, Content generation, Conversations or similar. The mission of the Intelligence team is to leverage Apollo’s massive scale data to understand and predict Apollo users’ behaviors and optimize their experience at all stages of their product journey.
Responsibilities:
- Design, build, evaluate, deploy and iterate on scalable Machine Learning systems
- Understand the Machine Learning stack at Apollo and continuously improve it
- Build systems that help Apollo personalize their users’ experience
- Evaluate the performance of machine learning systems against business objectives
- Develop and maintain scalable data pipelines that power our algorithms
- Implement automated monitoring, alerting, self-healing (restartable/graceful failures) features while productionizing data & ML workflows
- Write unit/integration tests and contribute to engineering wiki
Competencies:
- Documentation first approach; loves to scale up by writing things down to share knowledge asynchronously
- Excellent communication skills; be able to work with stakeholders to develop and define key business questions and build data sets that answer those questions.
- Excellent ambiguity resolution skills; be able to break down ambiguous problems into simpler milestones and delegate to junior engineers
- Self-motivated and self-directed
- Inquisitive, able to ask questions and dig deeper
- Organized, diligent, and great attention to detail
- Acts with the utmost integrity
- Genuinely curious and open; loves learning
- Critical thinking and proven problem-solving skills required
Required Qualifications:
- Bachelors, Masters, or a PhD in Computer Science, Mathematics, Statistics, or other quantitative fields or related work experience
- 8+ years of experience building Machine Learning or AI systems
- Experience deploying and managing machine learning models in the cloud
- Experience working with fine tuning LLMs and prompt engineering
- Strong analytical and problem-solving skills
- Proven software engineering skills in production environment, primarily using Python
- Experience with Machine Learning software tools and libraries (e.g., Scikit-learn, TensorFlow, Keras, PyTorch, etc.)
Preferred Qualifications:
- PhD in Computer Science or related field with a focus on machine learning
- Experience with Databricks, Google Cloud Platform, Snowflake, mlflow, and Airflow
- Experience with one or more of the following: natural language processing, deep learning, recommendation systems, search relevance & ranking, and speech-to-text conversion.
We are AI Native
Apollo.io is an AI-native company built on a culture of continuous improvement. We’re on the front lines of driving productivity for our customers—and we expect the same mindset from our team. If you're energized by finding smarter, faster ways to get things done using AI and automation, you'll thrive here.
Why You’ll Love Working at Apollo
At Apollo, we’re driven by a shared mission: to help our customers unlock their full revenue potential. That’s why we take extreme ownership of our work, move with focus and urgency, and learn voraciously to stay ahead.
We invest deeply in your growth, ensuring you have the resources, support, and autonomy to own your role and make a real impact. Collaboration is at our core—we’re all for one, meaning you’ll have a team across departments ready to help you succeed. We encourage bold ideas and courageous action, giving you the freedom to experiment, take smart risks, and drive big wins.
If you’re looking for a place where your work matters, where you can push boundaries, and where your career can thrive—Apollo is the place for you.
Learn more here!
Apollo
Search, engage, and convert over 210 million contacts at over 35 million companies with Apollo's sales intelligence and engagement platform.
See more jobsSafe Remote Job Search Tips
Verify Employer Thoroughly
Research the company's identity thoroughly before applying. Check for a professional website with contacts, active social media, and LinkedIn profiles. Verify details across platforms and look for reviews on Glassdoor or Trustpilot to confirm legitimacy.
Never Pay to Get a Job
Legitimate employers never require payment for applications, training, background checks, or equipment. Always reject upfront payment requests or demands for bank details, even if they claim it's for purchasing necessary work gear on your behalf.
Safeguard Your Personal Information
Protect sensitive data like SSN, bank details, or ID copies. Share this only after accepting a formal, written job offer. Ensure it's submitted via a secure company system or portal, never through insecure channels like standard email attachments.
Scrutinize Communication & Interviews
Watch for communication red flags: poor grammar, generic emails (@gmail), vague details, or undue pressure. Be highly suspicious of interviews held only via text or chat apps; legitimate companies typically use video or phone calls.
Beware of Unrealistic Offers
If an offer's salary or benefits seem unrealistically high for the work involved, be cautious. Research standard pay for similar roles. Offers that appear 'too good to be true' are often scams designed to lure you into providing information or payment.
Insist on a Formal Contract
Always secure and review a formal, written job offer or employment contract before starting work or sharing final personal details. Ensure it clearly defines your role, compensation, key terms, and conditions to avoid misunderstandings or scams.