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Ml Inference Jobs in Alameda, CA (NOW HIRING)

Parafin

Senior Software Engineer, ML Platform

San Francisco, CA · Remote

$220K - $265K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Decompose data scientist training/inference notebooks into reusable, tested components (libraries, pipelines, templates) with clear interfaces and documentation. * Create developer-friendly ML ...

General Motors

Staff AI/ML Engineer - AV ML Infra

Sunnyvale, CA

$218K - $335K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

AI Validation & Inference: Ensures robust model performance by running large-scale simulation workloads and managing reliable ML inference pipelines. * ML Compute: Streamlines andoptimizeslarge-scale ...

General Motors

Staff AI/ML Engineer - AV ML Infra

Sunnyvale, CA · On-site

$218K - $335K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

AI Validation & Inference: Ensures robust model performance by running large-scale simulation workloads and managing reliable ML inference pipelines. * ML Compute: Streamlines and optimizes large ...

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Showing results 21-40

All JobsMl Inference JobsMl Inference Jobs in Alameda, CA

Ml Inference information

See Alameda, CA salary details

$42.5K

$139.1K

$222.7K

How much do ml inference jobs pay per year?

As of Aug 19, 2026, the average yearly pay for ml inference in Alameda, CA is $139,107.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,600.00 and $154,100.00 per year, depending on experience, location, and employer.

What is ML inference?

ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.

What are the key skills and qualifications needed to thrive in ML inference?

To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.

What are some common challenges faced by ML inference engineers when deploying models to production?

ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.

What is the difference between Ml Inference vs Data Scientist?

AspectML InferenceData Scientist
Required CredentialsKnowledge of machine learning models, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentDeploying models in production, real-time data processingData analysis, model development, research
Industry UsageAI product deployment, software companiesResearch institutions, tech firms, consulting

ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.

What job categories do people searching Ml Inference jobs in Alameda, CA look for?

The top searched job categories for Ml Inference jobs in Alameda, CA are:

What cities near Alameda, CA are hiring for Ml Inference jobs?

Cities near Alameda, CA with the most Ml Inference job openings:

Ml Inference jobs near you

Senior Software Engineer, ML Platform

Parafin

San Francisco, CA • Remote

$220K - $265K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 15 days ago

Job description

About Us:

At Parafin, we’re on a mission to grow small businesses.
Small businesses are the backbone of our economy, but traditional banks often don’t have their backs. We build tech that makes it simple for small businesses to access the financial tools they need through the platforms they already sell on.
We partner with companies like DoorDash, Amazon, Worldpay, and Mindbody to offer fast and flexible funding, spend management, and savings tools to their small business users via a simple integration. Parafin takes on all the complexity of capital markets, underwriting, servicing, compliance, and customer service for our partners.
We’re a tight-knit team of innovators hailing from Stripe, Square, Plaid, Coinbase, Robinhood, CERN, and more — all united by a passion for building tools that help small businesses succeed. Parafin is backed by prominent venture capitalists including GIC, Notable Capital, Redpoint Ventures, Ribbit Capital, and Thrive Capital. Parafin is a Series C company, and we have raised more than $194M in equity and $340M in debt facilities.
Join us in creating a future where every small business has the financial tools they need.

About The Position

We’re looking for a software engineer to join Parafin’s Infrastructure team and lead the evolution of our ML Platform. This role is critical to building reliable, scalable, and developer-friendly systems for model experimentation, training, evaluation, inference, and retraining that power underwriting and other ML-driven products for small businesses.

As a Software Engineer, you’ll design, build, and maintain the core abstractions and platforms that let data scientists ship high-quality models to production—safely and quickly. You’ll partner closely with Data Science and Platform Engineering, own the ML platform end-to-end, and develop batch and real-time underwriting infrastructure.

What You'll Do

  • Turn notebooks into software. Decompose data scientist training/inference notebooks into reusable, tested components (libraries, pipelines, templates) with clear interfaces and documentation.

  • Create developer-friendly ML abstractions. Build SDKs, CLIs, and templates that make it simple to define features, train/evaluate models, and deploy to batch or real-time targets with minimal boilerplate.

  • Build our real-time ML inference platform. Stand up and scale low-latency model serving.

  • Expand batch ML inference. Improve scheduling, parallelism, cost controls, observability, and failure/rollback for large-scale batch scoring and post-processing.

  • Own and expand the feature store. Design offline/online feature definitions, high read/write throughput, and consistent offline/online semantics.

  • Platform reliability and observability. Instrument training/inference for latency, throughput, accuracy, drift, data quality, and cost; build alerting and dashboards; drive incident response and postmortems.

  • Underwriting infrastructure partnership. Support production batch and real-time underwriting systems in collaboration with Data Science; collaborate on model interfaces, SLAs, safety checks, and product integrations.

What We Are Looking For

  • 5+ years of software engineering experience, including experience on ML platform/MLOps systems (training, deployment, and/or feature pipelines).

  • Strong Python; solid software design and testing fundamentals. Proficiency with SQL; hands-on Spark/PySpark experience.

  • Knowledge of ML fundamentals—probability & statistics, supervised vs. unsupervised learning, bias/variance & regularization, feature engineering, model evaluation metrics, validation strategies, and production concerns like drift, stability, and monitoring.

  • Expertise with modern data/ML stacks—AWS, Databricks (workflows, lakehouse, MLflow/registry, Model Serving), and Airflow (or equivalent orchestration).

  • Experience building real-time systems (service design, caching, rate limiting, backpressure) and batch pipelines at scale.

  • Practical knowledge of feature-store concepts (offline/online stores, backfills, point-in-time correctness), model registries, experiment tracking, and evaluation frameworks.

  • Strong problem-solving skills and a proactive attitude toward ownership and platform health.

  • Excellent communication and collaboration skills, especially in cross-functional settings.

Bonus Points

  • Databricks experience (MLflow, Model Serving).

  • Experience with feature stores (e.g., Tecton, Feast) and streaming (Kafka/Kinesis).

  • Experience with fintech, risk, or underwriting systems; familiarity with model safety checks, rejection/override flows, and auditability.

  • Background with A/B testing platforms, shadow/canary deployments, and automated rollback.

  • Experience with low-latency inference systems.

What We Offer

  • Salary Range: $220k - $265k

  • Equity grant

  • Medical, dental & vision insurance

  • Work from home flexibility

  • Unlimited PTO

  • Commuter benefits

  • Free lunches

  • Paid parental leave

  • 401(k)

  • Employee assistance program

If you require reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please contact us.