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About This Role
Senior Software Engineer, ML Platform \| Parafin
*San Francisco, CA (Hybrid)*
*$230K\+Base with Competitive Equity*
*Visa Sponsorship Available (H\-1B Transfers \& O\-1\)*
Parafin is seeking a Senior Software Engineer, ML Platform to own and scale the infrastructure powering machine learning\-driven underwriting and financial products. This is a unique opportunity to build a critical ML platform from the ground up, enabling Data Scientists to efficiently develop, deploy, monitor, and scale production ML models that directly impact small businesses across partner ecosystems including Amazon, DoorDash, Walmart, and TikTok.
What You'll Do
- Architect and build the next generation ML Platform supporting experimentation, training, deployment, inference, monitoring, and retraining.
- Transform data science workflows into production\-grade software by building reusable libraries, pipelines, frameworks, SDKs, CLIs, templates, and developer tooling.
- Design and scale real\-time inference infrastructure, ensuring low latency, reliability, and high availability.
- Expand and optimize large\-scale batch inference systems, focusing on scheduling, observability, cost optimization, rollback capabilities, and operational excellence.
- Own and evolve the feature store ecosystem, including offline and online feature management, point\-in\-time correctness, high\-throughput access patterns, and feature governance.
- Drive platform reliability through monitoring, alerting, dashboards, incident response, model performance tracking, drift detection, data quality validation, and infrastructure observability.
- Partner closely with Data Science, Infrastructure, and Product teams to support underwriting systems, define model interfaces, establish SLAs, and implement production safeguards.
Required Qualifications
✔ 5\+ years of Software Engineering experience, including ML Platform, MLOps, Data Infrastructure, or Machine Learning Engineering environments.
✔ Strong software engineering fundamentals with expertise in:
- Python
- SQL
- Software architecture \& design
- Testing and code quality
✔ Hands\-on experience with:
- Spark / PySpark
- Databricks
- MLflow
- Airflow (or equivalent orchestration tools)
- AWS Cloud Services
✔ Experience building:
- Feature Stores
- Model Registries
- ML Deployment Pipelines
- Real\-Time Inference Systems
- Large\-Scale Data Processing Platforms
- Batch and Streaming Data Architectures
✔ Strong understanding of:
- ML lifecycle management
- Model evaluation \& validation
- Feature engineering
- Drift monitoring
- Experiment tracking
- Production ML best practices
- Probability \& Statistics fundamentals
✔ Proven ability to build scalable platforms that enable Data Scientists and ML Engineers to operate efficiently at scale.
Preferred Qualifications
Experience with:
- Kafka or Kinesis
- Feast, Tecton, or similar Feature Store technologies
- Databricks Model Serving
- Low\-latency model serving architectures
- A/B testing platforms
- Shadow deployments
- Canary releases
- Automated rollback frameworks
Domain expertise in:
- FinTech
- Lending
- Credit Risk
- Underwriting
- Risk Modeling
- Financial Services
Previous experience in startup or high\-growth environments where you've built systems from the ground up and influenced technical strategy.
Ideal Candidate
We're looking for an engineer who thinks beyond model development and focuses on building the infrastructure that makes ML teams successful. The ideal candidate has built reusable, scalable, software\-engineering\-grade platforms that empower data scientists to ship models safely and efficiently into production.
You'll thrive in this role if you enjoy solving platform\-scale challenges, influencing architectural direction, working in fast\-paced environments, and taking ownership of mission\-critical systems. Experience supporting high\-volume ML workloads and enabling cross\-functional teams is highly valued.
Why Join Parafin?
✅ Ground\-floor ownership of a critical ML Platform initiative
✅ Direct impact on financial products serving millions of users through leading technology platforms
✅ Opportunity to shape company\-wide ML architecture and infrastructure strategy
✅ Backed by top\-tier investors with over $194M raised
✅ High\-growth environment with significant investment in platform modernization and scalability
✅ Competitive compensation, equity participation, and visa sponsorship support
✅ Work alongside exceptional engineers, data scientists, and infrastructure leaders solving complex real\-world challenges.
Tech Stack
Python \| SQL \| Spark \| PySpark \| Databricks \| MLflow \| Airflow \| AWS \| Snowflake \| Kafka \| Kinesis \| Feature Stores \| Real\-Time Inference \| MLOps Platforms
If you're passionate about building world\-class ML infrastructure and enabling machine learning at scale, we'd love to hear from you.
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Pay: From $230,000\.00 per year
Benefits:
- Flexible schedule
- Relocation assistance
Application Question(s):
- "Do you have experience building and scaling ML platforms/MLOps systems, including model deployment, feature pipelines, real\-time/batch processing, and technologies such as Python, Spark/PySpark, AWS, Databricks, MLflow, and Airflow?" (Yes/No)
- Current Compensation ?
Expected Compensation?
- Do you possess all the required skills ;
Skills: Python, SQL, Spark/PySpark, AWS, Databricks, MLflow, Airflow, Feature Stores, Real\-Time \& Batch Pipelines, MLOps/ML Platforms. (Yes/No)
Experience:
- Software Engineering: 5 years (Required)
- ML Platform \& MLOps Infrastructure, Training, Deployment : 5 years (Required)
Work Location: Hybrid remote in San Francisco, CA 94114
Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 3,824 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At carnaby fox, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $217,200 based on 76 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
carnaby fox AI Hiring
carnaby fox has 1 open AI role right now. They're hiring across MLOps Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $253,000 across 1,990 tracked positions. That's 26% above the national median.
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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