📍 Job Overview

Job Title: Senior Machine Learning Platform Engineer (Hybrid)
Company: Homeward
Location: San Mateo, CA
Job Type: Hybrid
Category: Machine Learning & AI
Date Posted: June 11, 2025
Experience Level: 5-10 years

🚀 Role Summary

Revolutionize healthcare for rural America by addressing health inequities through machine learning.
Build and scale ML platforms to support experimentation, training, and evaluation at scale.
Collaborate with engineering, data, and product teams to create seamless and robust ML workflows.
Define architecture and best practices for ML development at Homeward, from experimentation to handoff.

💻 Primary Responsibilities

Platform Infrastructure: Design and build ML platform infrastructure to support experimentation, training, and evaluation at scale.
Internal Tools & APIs: Create internal tools and APIs to help data scientists work more efficiently.
Feature Engineering & Pipeline Orchestration: Build components for feature engineering, data access, and pipeline orchestration, integrating with Snowflake, dbt, and AWS-native tools.
Collaboration: Work closely with engineering and data teams to make ML workflows seamless and robust.
Third-Party Solutions: Evaluate and integrate third-party solutions (e.g., SageMaker, MLflow, Feature Stores) where appropriate.
Architecture & Best Practices: Define the architecture and best practices for ML development at Homeward.

🎓 Skills & Qualifications

Education: Bachelor's degree in Computer Science, Engineering, or a related field. Advanced degree preferred.

Experience: 5+ years of experience building ML platforms or scalable infrastructure in a production setting.

Required Skills:

Strong software engineering skills, especially in Python
Familiarity with ML frameworks (e.g., PyTorch, TensorFlow, XGBoost)
Deep experience with AWS services: SageMaker, S3, Lambda, Step Functions, etc.
Strong sense of ownership and collaborative mindset

Preferred Skills:

Experience supporting ML in healthcare, regulated data (HIPPA, PHI)
Familiarity with tools like MLflow, Metaflow, or open-source feature stores

📊 Web Portfolio & Project Requirements

ML Platform Projects: Showcase your experience building ML platforms or scalable infrastructure in previous projects.
AWS Projects: Highlight projects demonstrating your proficiency with AWS services, such as SageMaker, S3, Lambda, and Step Functions.
Collaboration Projects: Include examples of collaborative work with engineering, data, and product teams to create seamless and robust ML workflows.

💵 Compensation & Benefits

Salary Range: $150,000 - $200,000 per year (based on experience and location)

Benefits:

Competitive salary and equity grant
Generous paid time off
Comprehensive benefits package, including medical, dental, and vision insurance with 100% of monthly premium covered for employees
Company-sponsored 401k plan
Flexible working arrangement

🎯 Team & Company Context

Company Culture:

Deep commitment to one another, the people and communities served, and providing care that enables everyone to achieve their best health
Compassion and empathy
Curiosity and an eagerness to listen
Drive to deliver high-quality experiences, clinical care, and cost-effectiveness
Strong focus on the sustainability of the business and scalability of services to maximize reach and impact
Nurturing a diverse workforce with a wide range of backgrounds, experiences, and points of view
Taking the mission and business seriously but not taking themselves too seriously—having fun as they build!

Team Structure:

Collaborative environment with engineering, data, and product teams
Flat hierarchy with a strong focus on ownership and accountability

Development Methodology:

Agile development processes, focusing on experimentation, iteration, and continuous improvement
Data-driven decision-making and a strong emphasis on experimentation and A/B testing
Regular code reviews, testing, and quality assurance practices

Company Website: Homeward

📝 Enhancement Note: Homeward's mission and business model are aligned to address the healthcare, economic, and demographic challenges that make it challenging for rural Americans to stay healthy. The company's commitment to its mission and values creates a strong culture focused on delivering high-quality healthcare to rural communities in need.

📈 Career & Growth Analysis

Machine Learning Platform Engineer Role: This role involves designing, building, and scaling ML platforms to support experimentation, training, and evaluation at scale. It requires a strong background in software engineering, machine learning, and AWS services.

Reporting Structure: This role reports directly to the Head of Machine Learning Engineering and collaborates closely with engineering, data, and product teams.

Technical Impact: The Senior Machine Learning Platform Engineer will have a significant impact on Homeward's ability to deliver high-quality healthcare to rural communities by building and scaling ML platforms that support meaningful, real-world healthcare outcomes.

Growth Opportunities:

Technical Leadership: As an early hire, there are ample opportunities for growth and leadership in shaping Homeward's ML platform and engineering teams.
Career Progression: With Homeward's rapid growth, there will be opportunities for career progression into roles such as Principal Engineer, Engineering Manager, or Director of Engineering.
Learning & Development: Homeward encourages continuous learning and offers opportunities for professional development, including conference attendance, certification, and mentorship programs.

📝 Enhancement Note: Homeward's commitment to its mission and values creates an environment that fosters growth and development for its employees. The company's flat hierarchy and emphasis on ownership and accountability provide ample opportunities for employees to take on leadership roles and drive impact.

🌐 Work Environment

Office Type: Hybrid work environment with a focus on collaboration and in-person interaction.

Office Location(s): San Mateo, CA

Workspace Context:

Collaboration: Homeward's office space is designed to facilitate collaboration and communication between teams, with open workspaces and dedicated meeting areas.
Workstations: Each workstation is equipped with multiple monitors, allowing engineers to work efficiently on their projects.
Team Interaction: Homeward encourages cross-functional collaboration between teams, with regular team-building activities and social events.

Work Schedule: Homeward offers a flexible work schedule, with core hours between 10 AM and 3 PM PST to accommodate different work styles and time zones.

📝 Enhancement Note: Homeward's hybrid work environment and flexible work schedule allow employees to balance their personal and professional lives while maintaining a strong focus on collaboration and productivity.

📄 Application & Technical Interview Process

Interview Process:

Phone Screen: A brief call to discuss your background, experience, and interest in the role.
Technical Deep Dive: A comprehensive technical interview focused on your experience with ML platforms, AWS services, and software engineering best practices.
Cultural Fit & Team Interaction: A conversation with the team to assess cultural fit and team dynamics.
Final Decision: A final discussion with the hiring manager to make a decision on the candidate.

Portfolio Review Tips:

ML Platform Projects: Highlight your experience building ML platforms or scalable infrastructure, focusing on your role in designing, building, and scaling ML platforms to support experimentation, training, and evaluation at scale.
AWS Projects: Showcase your proficiency with AWS services, such as SageMaker, S3, Lambda, and Step Functions, by demonstrating your ability to build and manage ML workflows at scale.
Collaboration Projects: Include examples of collaborative work with engineering, data, and product teams to create seamless and robust ML workflows.

Technical Challenge Preparation:

ML Platform Design: Brush up on your knowledge of ML platform design principles, best practices, and architecture patterns.
AWS Services: Review your understanding of AWS services, focusing on SageMaker, S3, Lambda, and Step Functions.
Software Engineering: Refresh your software engineering skills, focusing on Python, data structures, and algorithms.

ATS Keywords: Machine Learning, Platform Engineering, Python, AWS, Data Access, Feature Engineering, Pipeline Orchestration, APIs, Collaboration, Software Engineering, ML Frameworks, SageMaker, MLflow, HIPAA, PHI

📝 Enhancement Note: Homeward's interview process is designed to assess the candidate's technical skills, cultural fit, and ability to collaborate with cross-functional teams. The company values candidates who are passionate about its mission and committed to delivering high-quality healthcare to rural communities.

🛠 Technology Stack & Web Infrastructure

Machine Learning Platform:

AWS Services: SageMaker, S3, Lambda, Step Functions, etc.
Data Storage & Processing: Snowflake, dbt
ML Frameworks: PyTorch, TensorFlow, XGBoost
ML Platform Tools: MLflow, Metaflow, open-source feature stores

📝 Enhancement Note: Homeward's technology stack is built on AWS services, with a focus on scalability, reliability, and ease of use. The company's commitment to open-source tools and best practices ensures that its ML platform is accessible, extensible, and maintainable.

👥 Team Culture & Values

Machine Learning Values:

Impact: Focus on delivering meaningful, real-world healthcare outcomes through ML platforms.
Collaboration: Work closely with engineering, data, and product teams to create seamless and robust ML workflows.
Iteration: Embrace an iterative approach to ML platform development, focusing on experimentation and continuous improvement.
Quality: Prioritize code quality, reliability, and maintainability in ML platform development.

Collaboration Style:

Cross-Functional Integration: Homeward encourages cross-functional integration between teams, with regular team-building activities and social events.
Code Review Culture: The company fosters a code review culture, with a strong emphasis on peer programming and knowledge sharing.
Mentorship & Learning: Homeward offers mentorship and learning opportunities to help employees grow both technically and professionally.

📝 Enhancement Note: Homeward's commitment to its mission and values creates a strong culture focused on delivering high-quality healthcare to rural communities. The company's emphasis on collaboration, iteration, and quality ensures that its ML platform is accessible, extensible, and maintainable.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Scalability: Design and build ML platform infrastructure to support experimentation, training, and evaluation at scale.
Integration: Evaluate and integrate third-party solutions (e.g., SageMaker, MLflow, Feature Stores) where appropriate.
Regulatory Compliance: Ensure that ML platforms comply with relevant regulations, such as HIPAA and PHI.

Learning & Development Opportunities:

Technical Skill Development: Stay up-to-date with the latest developments in machine learning, AWS services, and software engineering best practices.
Leadership Development: Develop your leadership skills by taking on more significant projects, mentoring junior engineers, and contributing to the company's strategic direction.
Career Progression: Pursue career progression opportunities within Homeward, such as Principal Engineer, Engineering Manager, or Director of Engineering.

💡 Interview Preparation

Technical Questions:

ML Platform Design: Describe your experience designing and building ML platforms or scalable infrastructure in a production setting.
AWS Services: Explain your proficiency with AWS services, such as SageMaker, S3, Lambda, and Step Functions, and how you've used them to build and manage ML workflows at scale.
Software Engineering: Discuss your software engineering skills, focusing on Python, data structures, and algorithms, and how you've applied them to build and maintain ML platforms.

Company & Culture Questions:

Mission & Values: Explain why you're drawn to Homeward's mission and how your personal values align with the company's.
Collaboration: Describe your experience working with cross-functional teams and how you've contributed to their success.
Iteration: Discuss your approach to ML platform development, focusing on experimentation, iteration, and continuous improvement.

Portfolio Presentation Strategy:

ML Platform Projects: Highlight your experience building ML platforms or scalable infrastructure, focusing on your role in designing, building, and scaling ML platforms to support experimentation, training, and evaluation at scale.
AWS Projects: Showcase your proficiency with AWS services, such as SageMaker, S3, Lambda, and Step Functions, by demonstrating your ability to build and manage ML workflows at scale.
Collaboration Projects: Include examples of collaborative work with engineering, data, and product teams to create seamless and robust ML workflows.

📌 Application Steps

To apply for this Senior Machine Learning Platform Engineer (Hybrid) position at Homeward:

Submit Your Application: Click the application link and submit your resume, highlighting your relevant experience, skills, and accomplishments.
Prepare Your Portfolio: Tailor your ML platform, AWS, and software engineering projects to demonstrate your ability to design, build, and scale ML platforms to support experimentation, training, and evaluation at scale.
Research Homeward: Familiarize yourself with Homeward's mission, values, and culture to ensure a strong fit and alignment with your personal values and career goals.
Prepare for Technical Interviews: Brush up on your knowledge of ML platform design principles, AWS services, and software engineering best practices to excel in the technical deep dive and challenge preparation stages.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and machine learning industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

Senior Machine Learning Platform Engineer (Hybrid)