---
title: "Machine Learning Engineer"
company: "Gatik AI"
company_url: "https://www.remjobs.works/companies/gatik-ai"
url: "https://www.remjobs.works/job/gatik-ai-machine-learning-engineer-bccbedb0-cb5b-4995-9629-d9f19869e1ba"
apply_url: "https://boards.greenhouse.io/gatikaiinc/jobs/4712524006?gh_jid=4712524006"
workplace: onsite
location: "Santa Clara, CA"
employment_type: unspecified
seniority: mid
role: ai-machine-learning
region: united-states
skills: ["azure", "cpp", "python", "pytorch", "tensorflow"]
date_posted: 2026-09-10T20:51:31.000Z
first_seen_by_remjobs: 2026-09-15T16:27:52.534Z
---

# Machine Learning Engineer

**Gatik AI** · Santa Clara, CA

Apply: https://boards.greenhouse.io/gatikaiinc/jobs/4712524006?gh_jid=4712524006

## About the role

**About the role**

We are seeking a high-impact, technically deep Machine Learning Engineer to develop, optimize, and deploy production ML models across our autonomous vehicle (AV) stack. This role is ideal for engineers who enjoy building models end-to-end - from data and training through optimization and real-time deployment on autonomous vehicles.

You will work closely with perception, prediction, planning, infrastructure, systems, and hardware teams to ensure models are efficient, scalable, reliable, and production-ready for both on-vehicle and cloud workflows.

This role is onsite 5 days a week at our Santa Clara, CA office!

**What you'll do**

- End-to-End Model Development: Own the full ML lifecycle, including data strategy, preprocessing, training, evaluation, optimization, deployment, and monitoring.

- Autonomous Driving Models: Develop and improve models supporting perception, prediction, planning, and scene understanding.

- Efficient Neural Network Design: Optimize models using techniques such as quantization, pruning, sparsification, compression, and efficient architecture design to meet strict latency, compute, memory, and power constraints.

- Real-Time Deployment: Integrate trained models into C++-based autonomy systems and optimize inference for production vehicle hardware.

- Model Optimization: Profile and optimize neural networks using CUDA, TensorRT, and related technologies.

- Simulation and Evaluation: Analyze model performance using simulation and real-world driving data, identify failure modes, and drive improvements.

- Scalable ML Infrastructure: Build high-throughput pipelines for training, evaluation, data processing, and large-scale offline inference.

- Data Workflows and Tooling: Develop reliable pipelines for dataset curation, annotation, preprocessing, visualization, diagnostics, benchmarking, and continuous feedback from field data.

- Cross-Functional Integration: Partner with autonomy, systems, hardware, and infrastructure teams to ensure ML components integrate reliably into the broader vehicle platform.

**What we're looking for**

- Education: MS or PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering, Statistics, Optimization, or a related field.

- Experience: Open to all experience levels. Leveling will be determined based on experience and technical depth.

- Programming & Frameworks:

- Strong Python skills and experience with frameworks such as PyTorch or TensorFlow.

- Strong C++ skills and experience integrating ML models into high-performance production systems.

- Core ML & Systems Expertise:

- Deep understanding of ML workflows, including data curation, training, evaluation, ablation studies, deployment, and inference optimization.

- Experience deploying and optimizing neural networks for real-time, embedded, robotics, autonomous driving, or other performance-constrained systems.

- Experience with model optimization techniques such as quantization, pruning, compression, and efficient architectures.

- Experience with software architecture, profiling, latency optimization, system-level debugging, and data flow analysis.

- Infrastructure & Compute Tools:

- Experience with CUDA and TensorRT is highly desirable.

- Experience with cloud-based ML training and evaluation pipelines, preferably Azure.

**Bonus Qualifications:**

- Experience with transformers, multimodal models, diffusion models, world models, or end-to-end driving models is a plus.

- Experience in autonomous driving, robotics, or other safety-critical real-time ML systems is strongly preferred.

- Publications or demonstrated technical contributions in efficient ML, autonomous driving, robotics, or related areas are a plus.

- Prior contributions to large-scale ML systems deployed in production.

**Salary Range** $170,000 - $240,000

---

Source: Gatik AI's own career page, read by RemJobs. Canonical HTML version: https://www.remjobs.works/job/gatik-ai-machine-learning-engineer-bccbedb0-cb5b-4995-9629-d9f19869e1ba