Sr./Staff Machine Learning Engineer
oscilar
If you want to move beyond coursework into real infrastructure work, this is a direct path. Oscilar is scaling ML systems that power financial decisions in real time. You'll own pieces of the pipeline that matter — from training to production — and work alongside data scientists and engineers who can teach you what production-grade ML actually looks like.
Your core work: scale and optimize existing ML systems for performance and cost. Build reproducible, automated pipelines for training and deployment. Collaborate with data scientists on model integration. You'll handle feature stores, model serving, and orchestration — systems that sit at the center of the platform.
This role fits engineers with solid fundamentals in machine learning or software engineering. You should be comfortable with Python, infrastructure thinking, and learning by doing. A portfolio of ML projects or relevant coursework helps. The title and scope are flexible depending on your experience level.
Apply through CareerJumpShip to submit your resume and a brief note on why you're interested in building ML infrastructure.
About this role
ABOUT OSCILAR Oscilar is building the next generation of AI-powered risk decisioning for fintech. Our platform helps financial institutions make faster, smarter decisions in real time. As we scale, machine learning sits at the core of what we do — and we are looking for an engineer who can help us build the infrastructure that makes it possible. THE ROLE We are hiring a Machine Learning Engineer to build, deploy, and maintain the ML infrastructure that powers Oscilar. You will own the systems that take models from development to production, and you will work closely with data scientists, platform engineers, and product teams to integrate ML capabilities throughout the Oscilar platform. Depending on your level of experience, this role can be filled at the Senior or Staff level. We are flexible on title and scope for the right person. WHAT YOU WILL DO - Scale and optimize existing ML systems. Improve the performance, reliability, and cost-efficiency of our current ML infrastructure, including feature stores, model serving, and orchestration pipelines. - Build reproducible, automated ML pipelines. Design and operate the pipelines that power model training, deployment, and monitoring across the platform — so models ship reliably and repeatably, not as one-off integrations. Partner with data scientists to make low-latency production deployment a paved path. - Build new ML infrastructure. Design and implement new components of our ML stack as the platform grows, with a focus on scalability, modularity, and developer experience. - Set ML engineering standards. Help define best practices for model deployment, monitoring, and lifecycle management. Mentor teammates and raise the bar across the organization. - Own production reliability. Be responsible for the uptime, performance, and correctness of ML systems serving real-time, business-critical decisions. WHAT WE ARE LOOKING FOR REQUIRED - 4+ years of experience building and maintaining production ML infrastructure. - Strong software engineering fundamentals, with experience designing distributed systems and writing high-quality, maintainable code. - Hands-on experience with the full ML lifecycle in production: feature engineering and serving, model deployment, monitoring, and retraining. - Proficiency in Scala and Python, with hands-on experience building data and ML workloads on distributed processing frameworks such as Spark and Flink. - Experience operating systems at scale, including performance tuning, observability, and incident response. - Strong communication skills and the ability to collaborate effectively across data science, engineering, and product teams. - Significant experience building and operating workloads on AWS. STRONGLY PREFERRED - Experience building ML infrastructure for fintech applications - Track record of scaling ML systems through significant growth in traffic, models, or feature volume. NICE TO HAVE - Prior experience as an ML engineer at a startup. BENEFITS - Compensation: Competitive salary and equity packages, including a 401k - Flexibility: Remote-first culture — work from anywhere - Health: 100% Employer covered comprehensive health, dental, and vision insurance with a top tier plan for you and your dependents (US) - Balance: Unlimited PTO policy - Technical: AI First company; both Co-Founders are engineers at heart; and over 50% of the company is Engineering and Product - Culture: Family-Friendly environment; Regular team events and offsites - Development: Unparalleled learning and professional development opportunities - Impact: Making the internet safer by protecting online transactions