Senior Machine Learning Engineer (Recommendations) at Philo

At Philo, we are a dynamic team of technology and product experts dedicated to creating the future of television. We combine cutting-edge technology with TV’s captivating power to build the ultimate viewing experience. Leveraging cloud delivery, modern tech stacks, machine learning, and native app experiences, we aim to deliver a robust streaming experience while innovating next-generation multi-screen and multi-user playback functionalities.

Job Description

Position: Senior Machine Learning Engineer (Recommendations)

Philo’s recommendation system enhances user engagement and boosts customer satisfaction by tailoring content discovery to individual preferences and viewing habits. Our goal is for users to always find something they want to watch when they open the app.

We are looking for a seasoned Machine Learning Engineer to lead the development of our recommendation system that serves millions of users. You will be a technical leader and subject matter expert, responsible for the entire stack and ensuring the system maximizes user engagement. Collaborating closely with data science, product, infrastructure, and backend engineering teams, you will contribute to creating enjoyable user experiences that drive customer acquisition and retention.

Responsibilities

• Design ML solutions for user problems prioritized by the team, considering project timelines and engineering resources.
• Develop and deploy machine learning models to enhance the accuracy and relevance of our recommendations.
• Identify the most impactful ways to integrate the recommendation system into the product.
• Conduct rigorous A/B testing and ML experiments to iterate rapidly based on feedback.
• Collaborate with cross-functional teams to integrate ML models into the production environment.
• Ensure the scalability and efficiency of ML systems, leveraging cloud technologies effectively.
• Contribute to the strategic planning of the recommendations roadmap, aligning with business objectives and user needs.

Qualifications

• 8+ years of experience in backend engineering and/or data science, with 4+ years focused on machine learning. Experience with recommendation systems is highly desirable.
• Strong proficiency in Python and ML frameworks like PyTorch or TensorFlow.
• Excellent analytical and problem-solving skills, capable of translating complex technical challenges into business solutions.
• Proven track record of leading projects and delivering impactful machine learning solutions.
• Strong communication and documentation skills, able to explain technical concepts to non-technical stakeholders and document work meticulously.

Nice to Have

• Experience with Amazon SageMaker or similar MLOps platforms.

Employment Details

Status: Full-time

Location: San Francisco, CA

Compensation: $200K - $240K annual salary, depending on experience and location, plus company stock options and health benefits.

Inclusive Workplace

Philo values diversity and is an equal opportunity employer. We welcome people of different backgrounds, experiences, skills, and perspectives. Our supportive environment ensures everyone does their best work, complemented by generous benefits that keep our team happy and healthy.

Benefits

• Full health, dental, and vision coverage for you and your family.
• 401(k) plan with employer contributions.
• Flexible working hours.
• Up to 20 weeks of fully paid parental leave.
• Unlimited paid time off for vacation and sick leave.
• $2,000 annual vacation bonus.
• $5,250 annually for professional development and educational assistance.
• $1,250 annual home office + TV stipend during the first year of employment ($250 annually thereafter).
• $500/month bonus for employees committed to working at least 3 days per week in our offices, plus generous commuter benefits.
• Free Gympass subscription.
• Dog-friendly office.
• And much more!

For California Residents: Philo’s .

Pursuant to the San Francisco Fair Chance Ordinance, we will consider qualified applicants with arrest and conviction records.