---
title: 'Senior Software Engineer - Machine Learning at Ocient Inc.'
canonical: 'https://feeny.ai/job/senior-software-engineer-machine-learning-ocient-inc-united-states-17f8dd2gr4rv'
type: 'job'
last_seen: '2026-09-10'
---

# Senior Software Engineer - Machine Learning at Ocient Inc.

- **Company:** Ocient Inc.
- **Location:** United States
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-08-07
- **Last confirmed live:** 2026-09-10
- **Apply:** https://jobs.gem.com/ocient-inc-/am9icG9zdDrjs1cXAvxZfz7oqTg028nU

## Job description

Job Title: Senior Software Engineer - Machine Learning Location: 100% Remote (US Based Only)

- We cannot sponsor or transfer any visas, of any kind, at this time* Hiring Manager: Senior Engineering Manager Estimated salary range: $165,000 to $190,000
- The salary offered for this position will be based on a candidate’s experience and skill demonstrated during interviews and other evaluations

Job Description: Ocient runs machine learning where the data lives. Models are trained and scored entirely inside the database through a native surface with no export, no separate Python stack, and no ETL round-trip. We support classification, regression, clustering, time series, neural network, ensemble, tree, pattern mining, and dimensionality reduction models today, and we are expanding that surface aggressively through 2026.

We are hiring a Senior Software Engineer to help build it. You will implement new model types, close feature and behavior gaps against the frameworks our customers already know, and make our ML run fast at scale. This is a hands-on engineering role on a small team with a lot of surface area, and you will own meaningful pieces of the roadmap end to end.

The work matters strategically. In-database ML is becoming table stakes across the industry, and our bet is on what comes next: in-database research, where optimization, spectral analysis, causal inference, simulation, and inverse modeling all become first-class declarative surfaces in the engine. The ML model catalog is the substrate that work stands on.

What You’ll Work On:

- Framework parity and behavioral correctness. Help close feature and semantic gaps against scikit-learn and Spark ML across the model catalog so our models behave the way users coming from those frameworks expect
- New model types and capabilities. Expand the model catalog across classification, regression, time series, and automated model selection, including loss functions and objectives we don't support today.
- Performance at scale. Make training and inference fast on datasets that don't fit anywhere else.  Approximate nearest-neighbor acceleration, distributed optimizer tuning, and algorithmic work on the hot paths.
- Numerical and linear-algebra foundations. Help expand the SQL-level linear algebra surface (SVD, eigenvalues, matrix inverse and solve, sparse matrices) and spectral transforms, the substrate under PCS, regression, and optimization.
- Architecture. Our models are compiled into the query plan and execute as a native part of it, rather than running in a separate ML runtime. You'll work inside that architecture and help improve it.
- Collaboration and craft. Write clear design docs, tests, and documentation; investigate issues where behavior diverges from user expectations; and partner with Product, architects, and customer-facing teams to identify gaps before customers hit them.

Qualifications:

- 5+ years building production software systems, including solid experience in C++ (or comparable systems-level work in Java/Scala with a willingness to work primarily in C++).
- Hands-on experience implementing or integrating machine learning models in production. You have written the training loop, not just called into a library.
- Working knowledge of numerical methods: gradient-based optimization, loss functions and their gradients, numerical stability, feature scaling, convergence behavior.
- Familiarity with scikit-learn, Spark ML, XGBoost, or comparable frameworks, and awareness of where their defaults and semantics matter.
- Strong instincts around correctness, edge cases, and behavioral consistency – and the discipline to encode them in tests and documentation.
- Ability to work across teams and codebases and turn ambiguous requirements into concrete solutions.

An Exceptional Candidate Will Have:

- Experience comparing or validating model behavior across multiple ML frameworks.
- Experience with large-scale data systems, analytical databases, query planners, or distributed execution engines.
- Exposure to optimization (LP/QP/SOCP), spectral methods (FFT/DCT/DWT), causal inference, or probabilistic modeling for the in-database research surface we are building next.
- Experience with automatic differentiation or symbolic gradient generation.
- Familiarity with SQL internals like AST manipulation, expression rewriting, or planner integration.

What Success Looks Like:

- Customers see fewer surprises. Our models behave the way someone coming from scikit-learn or Spark ML expects, and where they differ, the difference is intentional and documented.
- Roadmap items such as ARIMA, quantile regression, AutoML, option parity, land with validated correctness against reference implementations.
- Feature gaps are identified from benchmarks and product analysis early, not discovered under customer pressure.
- You deliver across both parity work and broader ML initiatives, balancing short-term needs with long-term quality.

## About Ocient Inc.

## Company Overview
- **One-liner**: Ocient delivers OcientAIQ, a unified data platform that brings trusted agentic AI directly to petabyte-scale enterprise data without moving it across fragmented systems.
- **Entity Type**: Private (venture-backed) – total funding $156.5M
- **Headquarters**: Chicago, Illinois, United States
- **Founded**: 2016
- **Founders**: Greg Papadopoulos (Co-Founder & VP of Customer Success & Quality), Joe Jablonski (Co-Founder & Chief Solutions Officer)

## Core Business
- Primary industries: Data & AI software, large-scale data analytics, agentic AI infrastructure
- Target customers: B2B Enterprise – organizations with massive datasets (government, national security, financial services, telecom, energy)
- Mission: “To deliver trusted agentic AI solutions that work in production at petabyte scale – helping the organizations doing the most consequential work in the world realize the full value of their data.”

## Products & Services
- **OcientAIQ™**: Complete ecosystem for building and deploying trusted agentic AI solutions at petabyte scale. Brings AI directly to data with built-in governance, compliance, and deployment options. Handles structured and semi-structured data in a single platform. (SaaS / platform)

## Market Standing
- **Valuation**: Not publicly disclosed (private company)
- **Key Metric**: Annual revenue ~$60M; total funding $156.5M (Series A, Series B, debt financing)
- **Notable Investors**: Greycroft, OCA Ventures, In-Q-Tel, Buoyant Ventures, TechNexus Venture Collaborative
- **Growth Signals**: Headcount 185 (+33.3% YoY); actively hiring with 10 open positions (+900% YoY in job postings); remote-first global workforce across 11 countries; carbon-neutral operations including LEED-certified data center; LinkedIn followers grew 29.6% YoY

## Competitive Advantages
- Founded by the team that built Cleversafe (acquired by IBM) – deep domain expertise in petabyte-scale data systems
- Purpose-built for organizations that cannot afford AI errors (national security, critical infrastructure)
- Unified platform eliminates data movement, reducing cost and latency at extreme scale
- Built-in trust and compliance controls for regulated industries
- Remote-first, carbon-neutral culture attracts top engineering talent

## Strategic Focus
- Scaling OcientAIQ ecosystem for enterprise adoption
- Expanding agentic AI capabilities (trusted, production-ready)
- Continuing international growth (India, Canada, Europe, Singapore)
- Heavy investment in engineering talent: open roles in distributed systems, ML, geospatial, SRE, QA automation, product management

## Why Work Here
- **Culture**: Craft-driven, autonomous, collaborative, inclusive, and ethical. “We are industry-leading experts … driven to deliver the best and brightest solutions.”
- **Work model**: Remote-first – team distributed across 11 countries
- **Impact**: Work on problems that protect networks, secure nations, and power the global economy
- **Compensation**: Sr. Software Engineer ~$172K/year; Software Engineer I ~$97K/year (LinkedIn data)
- **Perks**: Carbon-neutral operations, LEED-certified data center, commitment to UN Sustainable Development Goals
- **Rating**: 4.1/5.0 on LinkedIn (35 reviews) – culture and career score 4.2, work-life 4.1
- **Notable alumni destinations**: Google, Amazon, MongoDB, Citadel Securities

## Sources
1. [ocient.com/careers](https://ocient.com/careers/)
2. [linkedin.com/company/ocient](https://linkedin.com/company/ocient)
3. [ocient.com/about-ocient](https://ocient.com/about-ocient/)
4. [jobs.gem.com/ocient-inc-](https://jobs.gem.com/ocient-inc-)

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