Real Time Inference

Real Time Inference Developer Jobs

3 jobs found

1mo ago

Sr. Machine Learning Engineer, Pricing, Robotaxi

Tesla Palo Alto, CA

Palo Alto, CA

1mo ago Closed

$172k - $258k/yr Full time Artificial Intelligence Data Pipeline Data Processing

This position is no longer accepting applications (closed Oct 24, 2026).

Job Description

Build real-time pricing intelligence for Tesla’s Robotaxi division, using first-principles models to optimize customer experience alongside operational revenue and costs.

Responsibilities

Design, train, and productionize machine learning models for real-time Robotaxi pricing
Model key pricing inputs including base fare, distance and time, demand, traffic, tolls, taxes, government fees, and location-specific surcharges
Build price-elasticity, conversion, and competitive-response models
Develop dynamic and contextual pricing with logical guardrails to support rider trust and quote fairness
Productionize ML and optimization pipelines: feature engineering, training, low-latency inference, experimentation, monitoring, and rollback
Analyze ride sessions, costs, and competitor prices to continuously improve the pricing stack
Support global expansion with market-specific fees, airport, and event experiences
Partner with growth and profitability teams to help ensure fleet scaling remains economically viable

Requirements

Degree in Computer Science, Machine Learning, Operations Research, Economics, or a related field; or proof of exceptional skills in a related field; or equivalent experience
4+ years developing and deploying machine learning models and pricing or marketplace algorithms in large-scale production environments
Experience with rideshare, marketplace, or large-scale dynamic pricing strongly preferred
Proficiency in Python and at least one of Go, C++, Java, or Scala
Experience building backend distributed systems and low-latency, user-facing services
Experience with deep learning, causal machine learning, and/or reinforcement learning for pricing or real-time decisioning
Experience with large-scale data systems and ML/RL for pricing and real-time decisioning
Strong experimentation instincts, first-principles economics, and high standards for optimal outcomes
Ability to solve open-ended, difficult technical problems with little guidance
Excellent collaboration across product, finance, billing, and engineering

Technologies

Python
Go
C++
Java
Scala

Compensation

$172,000 - $258,000 per year salary + cash and stock awards + benefits.

Pay offered may vary based on individualized factors including market location, job-related knowledge, skills, and experience. Total compensation may include other elements depending on the position offered.

Location

Palo Alto, CA (onsite)

Benefits

Medical plans with $0 payroll deduction
Family-building, fertility, adoption and surrogacy benefits
Dental (including orthodontic coverage) and vision plans with options and $0 paycheck contribution
Company Paid HSA Contribution when enrolled in the High-Deductible medical plan with HSA
Healthcare and Dependent Care Flexible Spending Accounts (FSA)
401(k) with employer match, Employee Stock Purchase Plans, and other financial benefits
Company paid Basic Life and AD&D
Short-term and long-term disability insurance (90 day waiting period)
Employee Assistance Program
Sick and Vacation time (Flex time for salary positions, Accrued hours for Hourly positions) and Paid Holidays
Back-up childcare and parenting support resources
Voluntary benefits: critical illness, hospital indemnity, accident insurance, theft and legal services, and pet insurance
Weight Loss and Tobacco Cessation Programs
Tesla Babies program
Commuter benefits
Employee discounts and perks program

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1mo ago

Software Engineer III, ML, TPU Efficiency, YouTube

YouTube Mountain View, CA

Mountain View, CA

1mo ago Closed

$147k - $210k/yr Full time Ai Accelerators Data Pipeline Data Processing

This position is no longer accepting applications (closed Oct 24, 2026).

Job Description

YouTube builds the machine learning systems that power the YouTube algorithm, and this role focuses on making those models more efficient across training and serving. You will help reduce training and serving costs while improving fleet utilization by working at the intersection of ML model optimization and TPU compiler co-design. The position is based in Mountain View, CA (onsite).

Compensation: USD 147,000 - 210,000 per year, plus 15% bonus target, equity, and benefits.

Responsibilities

Profile ML workloads to find compute and memory bandwidth bottlenecks, then optimize accelerator utilization to maximize compute efficiency.
Explore, implement, and productionize algorithmic efficiency techniques such as low-precision quantization, knowledge distillation, parameter sharing, and attention optimizations.
Optimize auxiliary serving and distributed data pipelines, including data ingestion, feature transformation, embedding lookups, and memory caching to support real-time training and inference.
Collaborate with ML model developers and researchers to co-design hardware-friendly model architectures and deploy universal efficiency libraries.

Requirements

Bachelor's degree or equivalent practical experience.
2 years of experience programming in C++ or Python.
2 years of experience with software design and architecture.
2 years of experience testing and launching software products.
Experience with ML model optimization.
Experience with ML frameworks such as TensorFlow, JAX, and PyTorch, or ML compilers such as accelerated linear algebra (XLA).

Preferred Qualifications

Master's degree or PhD in Computer Science or related technical fields.
Experience developing accessible technologies.
Experience debugging correctness and performance issues across the full ML software stack.
Experience with ML compilers and their internals, including writing compiler optimization passes.
Familiarity with accelerator hardware architectures (TPUs/GPUs).

Tech Stack

C++, Python
TensorFlow, JAX, PyTorch
XLA
TPUs, GPUs

In most instances, this position requires in-person interviews as part of the hiring process.

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1mo ago

Machine Learning Engineer

Liquid XR Los Angeles, CA

Los Angeles, CA

1mo ago Closed

Full time Ai Ml Artificial Intelligence Data Pipeline

This position is no longer accepting applications (closed Sep 29, 2026).

Job Description

LiquidXR is seeking a Machine Learning Engineer to help build advanced models that turn multimodal time-series sensor data into meaningful signals. In this hybrid role based in Los Angeles, you will focus on robust real-time algorithms that perform reliably with noisy, high-frequency inputs.

The work spans modeling, training strategy, and practical deployment, from research-style experimentation through to production-grade inference under latency and compute constraints.

What you’ll do

Design and implement machine learning models for time-series and sequential data
Develop algorithms to extract structured signals and latent variables from noisy sensor inputs
Build and optimize real-time inference pipelines with latency and compute constraints
Apply multi-modal learning and sensor fusion techniques
Replace or augment classical signal processing pipelines with learned models
Design training strategies for windowed and streaming data
Design training strategies for weakly labeled or partially observed datasets
Design training strategies for multi-task learning setups
Evaluate models using both statistical metrics and application-driven performance criteria
Collaborate with cross-functional teams to take models from research to production
Explore architectures including Temporal Convolutional Networks (TCNs)
Explore architectures including RNNs, LSTMs, and GRUs
Explore architectures including Transformer-based sequence models

What you bring

Strong experience with machine learning for time-series data
Experience with transfer learning and knowledge distillation techniques
Proficiency in Python and PyTorch (or similar frameworks)
Solid understanding of signal processing fundamentals including filtering, noise, and the frequency domain
Experience working with real-world, noisy datasets
Experience building or deploying low-latency or real-time systems
Experience with sensor data such as IMUs
Familiarity with sensor fusion methods such as Kalman filters and probabilistic models
Experience with multi-modal or multi-task learning
Exposure to embedded or edge deployment constraints
Background in applied domains involving physical systems or human data
BSc or MSc in quantitative fields such as computer science, engineering, physics, or applied math
Ability to reason about temporal structure, causality, and latency
Strong intuition for modeling tradeoffs versus deployment constraints
Comfort working with imperfect, real-world data
End-to-end ownership from modeling through validation to deployment
Self-starter mindset with the ability to catch the vision and run with it
Clear communicator and effective collaborator with cross-functional teams
Ability to manage multiple priorities without sacrificing quality
Detail-oriented approach focused on high-quality and well-documented work
Ownership mindset with full accountability from concept to completion

Tools and technologies

Python
PyTorch
RNNs
LSTMs
GRUs
Transformer-based sequence models
Temporal convolutional networks (TCNs)
Kalman filters

Benefits

Employee stock option program
Health care benefits: currently, gold PPO coverage with Blue Shield, plus dental and vision, starting within 30 days of employment
Open PTO company

Location: Los Angeles, CA (hybrid). Employment: Full-time employee position, working remotely or in the Los Angeles office. Compensation: Commensurate with experience and competitive with the market. Travel: Occasional domestic and international travel may be required.

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Real Time Inference Developer Jobs

Sr. Machine Learning Engineer, Pricing, Robotaxi

Software Engineer III, ML, TPU Efficiency, YouTube

Machine Learning Engineer

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