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About the role
Key responsibilities & impact- Build and improve ML components across data, training, evaluation, and inference.
- Fine-tune and adapt models as part of larger production systems.
- Implement evaluation and testing to understand model behavior.
- Help build and maintain data pipelines for real-world and synthetic data.
- Debug model issues, performance problems, and production incidents.
- Ship improvements iteratively and learn from real user feedback.
- Work closely with senior ML engineers and product teams.
- Work under real production constraints: latency, cost, reliability, and safety
Requirements
What you’ll need- Strong foundations in machine learning and modern neural architectures.
- Some hands-on experience training, fine-tuning, or deploying ML models.
- Comfortable writing production-quality code and learning new tools quickly.
- Curious, coachable, and eager to learn from real systems in production.
- Able to work through ambiguity with guidance and grow ownership over time.
- Bias toward shipping, iteration, and continuous improvement.
Benefits
Comp & perks- Health insurance
- Professional development
- Flexible working arrangements
- Remote work options
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningneural architecturesmodel trainingmodel fine-tuningmodel deploymentdata pipelinesmodel evaluationproduction-quality codedebuggingperformance optimization
Soft Skills
curiositycoachabilityeagerness to learnambiguity resolutionownershipiterationcontinuous improvementcollaborationfeedback incorporationproblem-solving