Software Engineer – Machine Learning
Launch Your Career in AI-Powered Systems
📍 Position Details
Location: United States (Remote, Hybrid, or On-Site options available)
Employment Type: Full-Time, Salaried
Experience Level: Entry-Level to Early Career (1–3 years)
Visa Sponsorship: H-1B sponsorship available for qualified candidates
STEM OPT: F-1 STEM OPT candidates strongly encouraged to apply
Salary Range: $90K–$130K (depending on experience, skills, and location)
🚀 About the Role
We're seeking a motivated early-career Software Engineer with Machine Learning expertise to join our growing team. This is an excellent opportunity for entry level candidates and professionals with 1–3 years of experience who are passionate about applying ML in real-world production environments.
You'll work alongside experienced engineers and data scientists, gaining hands-on experience in building scalable ML systems while contributing to meaningful projects from day one.
💼 Key Responsibilities
ML Model Development & Deployment
Assist in developing and deploying machine learning models to production
Build and maintain ML pipelines for data processing and model training
Implement model evaluation metrics and monitoring dashboards
Support A/B testing and model performance analysis
Software Engineering
Develop backend services and APIs using Python
Write clean, well-tested code following team standards and best practices
Participate in code reviews and learn from senior engineers
Debug and troubleshoot production issues
MLOps & Infrastructure
Help build CI/CD pipelines for ML model deployment
Work with Docker and Kubernetes for containerized applications
Implement data validation and feature engineering workflows
Use MLOps tools for experiment tracking and model versioning
Learning & Collaboration
Collaborate with data scientists to understand model requirements
Work with cross-functional teams in an Agile environment
Stay updated on emerging ML technologies and best practices
Participate in team knowledge-sharing sessions and workshops
🎯 Required Qualifications
Education
Bachelor's degree in Computer Science, Software Engineering, Data Science, Machine Learning, Artificial Intelligence, Mathematics, Statistics, or related STEM field
Master's degree is a plus but not required
entry level candidates (Class of 2022–2024) are encouraged to apply
Experience
1–3 years of relevant experience (internships, co-ops, academic projects, or professional work)
Demonstrated experience through personal projects, GitHub repositories, or contributions to open-source
Technical Skills
Programming:
Proficiency in Python (NumPy, Pandas, object-oriented programming)
Working knowledge of SQL and relational databases
Familiarity with Git and version control workflows
Understanding of software development fundamentals and data structures
Machine Learning:
Hands-on experience with TensorFlow, PyTorch, or Scikit-learn (through coursework or projects)
Understanding of ML fundamentals: regression, classification, neural networks, model evaluation
Experience with data preprocessing, feature engineering, and exploratory data analysis
Basic knowledge of model training, validation, and testing workflows
Cloud & Tools:
Basic familiarity with at least one cloud platform (AWS, Azure, or Google Cloud Platform)
Exposure to Docker and containerization concepts
Understanding of REST APIs and web services
Experience with Jupyter Notebooks and ML development environments
MLOps (Preferred but trainable):
Awareness of CI/CD concepts
Familiarity with MLflow, Weights & Biases, or similar experiment tracking tools
Basic understanding of Kubernetes or willingness to learn
⭐ Preferred Qualifications
Internship experience at a tech company or ML-focused role
Academic research in machine learning or related fields
Kaggle competitions or similar ML challenge participation
Experience with NLP, Computer Vision, or Recommender Systems
Contributions to open-source projects or active GitHub profile
Familiarity with Linux/Unix environments
Strong communication skills and ability to explain technical concepts
Demonstrated passion for ML through side projects or continuous learning
🌟 What We Offer
Compensation & Benefits
Competitive salary: $90K–$130K based on experience and location
Annual performance bonuses
Stock options/equity (for eligible positions)
Comprehensive health insurance (Medical, Dental, Vision) starting day one
401(k) retirement plan with company matching
HSA/FSA options
Work-Life Balance
20 days PTO plus 11 federal holidays
Paid sick leave
Paid parental leave
Flexible work arrangements (remote/hybrid available)
$500 home office setup stipend for remote employees
Professional Development & Growth
Mentorship program – paired with senior ML engineers
$2,000 annual learning budget for courses, books, and certifications
Access to O'Reilly Learning Platform, Coursera, Udemy
Conference attendance opportunities (NeurIPS, ICML, PyData, etc.)
Clear career progression pathway (Mid-Level Engineer within 2–3 years)
Regular 1-on-1s with manager for career development
Early-Career Support
Structured onboarding program (first 90 days)
Regular training sessions on ML best practices and tools
Exposure to diverse projects and technologies
Collaborative team environment that values learning
Brown bag lunch sessions with industry experts
Additional Perks
Latest MacBook Pro or Linux workstation
Premium development tools and software licenses
Team social events and activities
Immigration support (H-1B, sponsorship)
Relocation assistance up to $5,000 (if applicable)
🛂 Immigration & Visa Information
We actively support international talent:
H-1B Sponsorship: Available for candidates with bachelor's degree or higher in STEM fields
STEM OPT Extension: F-1 students eligible for 24-month STEM OPT extension
CPT: Current students on CPT are welcome to apply for internship-to-hire pathways
Sponsorship: Available for long-term employees after 1–2 years
Cap-Gap Extension: Support provided for OPT to H-1B transitions
All applicants must be authorized to work in the United States or be eligible for visa sponsorship
📚 What Success Looks Like
First 30 Days
Complete onboarding and set up development environment
Understand existing ML pipelines and infrastructure
Ship first small feature or bug fix
Meet with team members and stakeholders
First 90 Days
Independently develop and deploy ML features to production
Contribute to code reviews and technical discussions
Complete assigned training modules
Begin working on medium-sized projects
First Year
Own end-to-end ML features from design to deployment
Mentor new team members or interns
Contribute to technical documentation and best practices
Present learnings at team meetings or internal tech talks
📋 Application Process
Submit Application: Resume + brief cover letter or statement of interest
Recruiter Screen: 20–30 minute phone call to discuss background and interests
Technical Assessment:
Take-home coding challenge (ML-focused, 2–3 hours) OR
Live coding session (60 minutes)
Technical Interviews: 2 rounds
Round 1: Python coding + ML fundamentals (60 min)
Round 2: System design basics + ML concepts (60 min)
Behavioral Interview: Team fit and culture discussion (45 min)
Offer Decision: Typically within 1 week of final interview
Total Timeline: 2–3 weeks from application to offer
We understand early-career candidates are still learning – we evaluate based on potential, problem-solving ability, and growth mindset, not just years of experience.
🤝 Equal Opportunity Employer
We are committed to building a diverse team and creating an inclusive workplace. We encourage applications from candidates of all backgrounds, including underrepresented groups in tech.
We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, age, or any other protected characteristic.
Reasonable accommodations available during the application process for candidates with disabilities.
📧 How to Apply
Ready to launch your ML engineering career? We'd love to hear from you!
Submit:
Resume/CV
Brief cover letter or statement (3–5 sentences about why you're interested)
Link to GitHub profile or portfolio (optional but highly encouraged)
Any relevant projects, papers, or Kaggle profiles
Current visa/work authorization status
[Apply Now Button]
Applications reviewed on a rolling basis. Early applications encouraged!
💡 Tips for Applicants
Showcase your projects: Even if you have limited work experience, strong personal or academic projects demonstrate your skills
Highlight learning: We value candidates who show curiosity and continuous learning
Be authentic: We want to understand your genuine interest in ML and software engineering
Prepare questions: We love candidates who ask thoughtful questions about our tech stack and culture
Never miss an opportunity! Create an alert based on the job you applied for.
